{
  "id": 665022,
  "title": "Handling the pivot table with 7140 missing values – is filling with 0.0 reasonable or should we try interpolation?",
  "url": "/competitions/csiro-biomass/discussion/665022",
  "author_name": "",
  "post_date": "2025-12-29T16:47:20.004153100Z",
  "votes": 3,
  "comment_count": 12,
  "views": 0,
  "content": "<p>Hi everyone,\nFirst of all, thank you to the hosts for a very interesting competition!</p>\n<p>While creating this wide-format table I found that 7140 missing values in pivot table. So filling with 0 might make domain sense for biomass (no observation = no biomass at that point in time?).</p>\n<p>Still, 7140 imputed zeros feel like a lot, and I'm worried this strong imputation choice is harming model convergence and generalization.</p>\n<p>I want to ask that:</p>\n<ol>\n<li><p>Do you think filling with 0.0 is conceptually okay in this setting, or does it introduce too much bias/noise?</p></li>\n<li><p>Have people tried alternative strategies that worked better? Some ideas I’m considering:</p>\n<ul>\n<li>Forward-fill / backward-fill per paddock (but this could leak future information if not done carefully with group-wise time ordering)</li>\n<li>Linear or spline interpolation per paddock/time-series</li>\n<li>Filling only early/late missing values with 0 and interpolating in the \"middle\" of each series</li>\n<li>Masking the missing values during loss computation (custom masked MSE / MAE) instead of imputing</li>\n<li>Dropping paddocks / time points with too many missing values</li>\n<li>Using models that natively handle missing data (e.g. certain GBDTs or time-series libraries that support NaNs)</li></ul></li>\n<li><p>Did anyone who reached a decent leaderboard score keep the 0-filling strategy, or did you move away from it?</p></li>\n</ol>\n<p>Very grateful for any experiences, lessons learned, or suggestions.</p>\n<p>Thanks in advance, and happy competing! </p>\n<p>Best regards,\nNguyen Cong Tuan</p>",
  "messages": [
    {
      "id": "3383222",
      "postDate": "12/29/2025 16:47:20",
      "content": "<p>Hi everyone,\nFirst of all, thank you to the hosts for a very interesting competition!</p>\n<p>While creating this wide-format table I found that 7140 missing values in pivot table. So filling with 0 might make domain sense for biomass (no observation = no biomass at that point in time?).</p>\n<p>Still, 7140 imputed zeros feel like a lot, and I'm worried this strong imputation choice is harming model convergence and generalization.</p>\n<p>I want to ask that:</p>\n<ol>\n<li><p>Do you think filling with 0.0 is conceptually okay in this setting, or does it introduce too much bias/noise?</p></li>\n<li><p>Have people tried alternative strategies that worked better? Some ideas I’m considering:</p>\n<ul>\n<li>Forward-fill / backward-fill per paddock (but this could leak future information if not done carefully with group-wise time ordering)</li>\n<li>Linear or spline interpolation per paddock/time-series</li>\n<li>Filling only early/late missing values with 0 and interpolating in the \"middle\" of each series</li>\n<li>Masking the missing values during loss computation (custom masked MSE / MAE) instead of imputing</li>\n<li>Dropping paddocks / time points with too many missing values</li>\n<li>Using models that natively handle missing data (e.g. certain GBDTs or time-series libraries that support NaNs)</li></ul></li>\n<li><p>Did anyone who reached a decent leaderboard score keep the 0-filling strategy, or did you move away from it?</p></li>\n</ol>\n<p>Very grateful for any experiences, lessons learned, or suggestions.</p>\n<p>Thanks in advance, and happy competing! </p>\n<p>Best regards,\nNguyen Cong Tuan</p>",
      "rawMarkdown": "Hi everyone,\nFirst of all, thank you to the hosts for a very interesting competition!\n\nWhile creating this wide-format table I found that 7140 missing values in pivot table. So filling with 0 might make domain sense for biomass (no observation = no biomass at that point in time?).\n\nStill, 7140 imputed zeros feel like a lot, and I'm worried this strong imputation choice is harming model convergence and generalization.\n\nI want to ask that:\n1. Do you think filling with 0.0 is conceptually okay in this setting, or does it introduce too much bias/noise?\n\n2. Have people tried alternative strategies that worked better? Some ideas I’m considering:\n    - Forward-fill / backward-fill per paddock (but this could leak future information if not done carefully with group-wise time ordering)\n    - Linear or spline interpolation per paddock/time-series\n    - Filling only early/late missing values with 0 and interpolating in the \"middle\" of each series\n    - Masking the missing values during loss computation (custom masked MSE / MAE) instead of imputing\n    - Dropping paddocks / time points with too many missing values\n    - Using models that natively handle missing data (e.g. certain GBDTs or time-series libraries that support NaNs)\n\n3. Did anyone who reached a decent leaderboard score keep the 0-filling strategy, or did you move away from it?\n\nVery grateful for any experiences, lessons learned, or suggestions.\n\nThanks in advance, and happy competing! \n\nBest regards,\nNguyen Cong Tuan",
      "votes": null
    },
    {
      "id": "3383225",
      "postDate": "12/29/2025 16:51:21",
      "content": "<p>Not sure what your code is doing but I don't think you should be seeing any nan - when I have had them in my pivot I found mistakes in the creation of the data.</p>",
      "rawMarkdown": "Not sure what your code is doing but I don't think you should be seeing any nan - when I have had them in my pivot I found mistakes in the creation of the data.",
      "votes": null
    },
    {
      "id": "3383229",
      "postDate": "12/29/2025 17:01:15",
      "content": "<p>Thank you for the quick reply!\nThat's interesting - so in your case, after properly constructing the pivot / wide table you ended up with (almost) no NaNs at all?</p>",
      "rawMarkdown": "Thank you for the quick reply!\nThat's interesting - so in your case, after properly constructing the pivot / wide table you ended up with (almost) no NaNs at all?",
      "votes": null
    },
    {
      "id": "3383234",
      "postDate": "12/29/2025 17:05:04",
      "content": "<p>None - your table sound much larger than mine - dont think my pivot would have been big enough for 7140 nan's.</p>",
      "rawMarkdown": "None - your table sound much larger than mine - dont think my pivot would have been big enough for 7140 nan's.",
      "votes": null
    },
    {
      "id": "3383238",
      "postDate": "12/29/2025 17:09:04",
      "content": "<p>You must be pivoting more than just the 5 labels - generating some type stats in the pivot - mean, etc. ??</p>\n<p>if you don't think you notebook is the magic winner you might make it public and it would be easier for folks to help.</p>\n<p>For this competition putting nan's in a 0.0 would likely be a mistake.  I tried finding some bare soil images that would have 0.0 for all the labels - just a few of them made my issues worse - my major model failure is big errors on the larger biomass images - always under predicting.  More 0.0 in the data would make the model even more inclined to have that error.</p>",
      "rawMarkdown": "You must be pivoting more than just the 5 labels - generating some type stats in the pivot - mean, etc. ??\n\nif you don't think you notebook is the magic winner you might make it public and it would be easier for folks to help.\n\nFor this competition putting nan's in a 0.0 would likely be a mistake.  I tried finding some bare soil images that would have 0.0 for all the labels - just a few of them made my issues worse - my major model failure is big errors on the larger biomass images - always under predicting.  More 0.0 in the data would make the model even more inclined to have that error.",
      "votes": null
    },
    {
      "id": "3383239",
      "postDate": "12/29/2025 17:12:31",
      "content": "<p>If your using one of the LLM like ChatGPT to help you code - show it the cell of code and copy part of the table where nan's are shown - than tell it your certain that nan's are wrong - whats my mistake.</p>\n<p>If you share your notebook that's probably the exact thing I will do to help you :)</p>",
      "rawMarkdown": "If your using one of the LLM like ChatGPT to help you code - show it the cell of code and copy part of the table where nan's are shown - than tell it your certain that nan's are wrong - whats my mistake.\n\nIf you share your notebook that's probably the exact thing I will do to help you :)",
      "votes": null
    },
    {
      "id": "3383246",
      "postDate": "12/29/2025 17:20:34",
      "content": "<p>Thank you for your enthusiasm. I think we can consider on tomorrow 'cause it's 00:00 AM in VietNam :)</p>",
      "rawMarkdown": "Thank you for your enthusiasm. I think we can consider on tomorrow 'cause it's 00:00 AM in VietNam :)",
      "votes": null
    },
    {
      "id": "3383247",
      "postDate": "12/29/2025 17:22:42",
      "content": "<p>What the heck - most of my best thinking occurs on several cups of coffee after midnight !</p>\n<p>Good luck tomorrow !</p>",
      "rawMarkdown": "What the heck - most of my best thinking occurs on several cups of coffee after midnight !\n\nGood luck tomorrow !",
      "votes": null
    },
    {
      "id": "3383331",
      "postDate": "12/29/2025 22:24:40",
      "content": "<p>You should have linked your notebook into this discussion.  Just found it, and see that your initial lines of output show a number of missing values - will check it this evening - sometime before midnight :)   Maybe I can find what I think is an error.</p>",
      "rawMarkdown": "You should have linked your notebook into this discussion.  Just found it, and see that your initial lines of output show a number of missing values - will check it this evening - sometime before midnight :)   Maybe I can find what I think is an error.",
      "votes": null
    },
    {
      "id": "3383336",
      "postDate": "12/29/2025 22:46:24",
      "content": "<p>First issue - the df_wide has NaT for the Sampling_date - this ends up in the nan count.  If the NaT had been real not sure what the best method would have been to fillna - never did missing dates that I can recall.</p>\n<p>Anyway - This line generates that error</p>\n<p><code>df_wide['Sampling_Date'] = pd.to_datetime(df_wide['Sampling_Date'], format='%m/%d/%Y', errors='coerce')</code></p>\n<p>The data supplied is in year month day format - coerce generates an NaT error since the format of the data does not match your \"requirement'.  I replaced it with this and about half the missing went away.</p>\n<p><code>df_wide['Sampling_Date'] = pd.to_datetime(df_wide['Sampling_Date'], dayfirst=True, errors='coerce')</code></p>\n<p>But for some reason there were still some NaT in the sample date column of wide.   So I replaced it with this attempt</p>\n<pre><code>df_wide['Sampling_Date'] = pd.to_datetime(df_wide['Sampling_Date'], format='%Y/%m/%d', errors='coerce')\n</code></pre>\n<p>That eliminated all the missing shown in the initial output</p>\n<p>Data Quality Summary:</p>\n<ul>\n<li>Missing values: 0</li>\n<li>States: ['Tas', 'NSW', 'WA', 'Vic']</li>\n<li>Date range: 2015-01-15 00:00:00 to 2015-11-10 00:00:00</li>\n</ul>\n<p>Still have not looked over all the output to see if this was the source of all your NAN's.</p>\n<p>Have written code (as a hobbist) in a bunch of programming languages over my lifetime.  </p>\n<p>Handling dates ALWAYS SUCKS.   </p>",
      "rawMarkdown": "First issue - the df_wide has NaT for the Sampling_date - this ends up in the nan count.  If the NaT had been real not sure what the best method would have been to fillna - never did missing dates that I can recall.\n\nAnyway - This line generates that error\n\n`df_wide['Sampling_Date'] = pd.to_datetime(df_wide['Sampling_Date'], format='%m/%d/%Y', errors='coerce')`\n\nThe data supplied is in year month day format - coerce generates an NaT error since the format of the data does not match your \"requirement'.  I replaced it with this and about half the missing went away.\n\n`df_wide['Sampling_Date'] = pd.to_datetime(df_wide['Sampling_Date'], dayfirst=True, errors='coerce')`\n\nBut for some reason there were still some NaT in the sample date column of wide.   So I replaced it with this attempt\n\n```python\ndf_wide['Sampling_Date'] = pd.to_datetime(df_wide['Sampling_Date'], format='%Y/%m/%d', errors='coerce')\n\n```\nThat eliminated all the missing shown in the initial output\n\nData Quality Summary:\n  - Missing values: 0\n  - States: ['Tas', 'NSW', 'WA', 'Vic']\n  - Date range: 2015-01-15 00:00:00 to 2015-11-10 00:00:00\n\nStill have not looked over all the output to see if this was the source of all your NAN's.\n\nHave written code (as a hobbist) in a bunch of programming languages over my lifetime.  \n\nHandling dates ALWAYS SUCKS.",
      "votes": null
    },
    {
      "id": "3383341",
      "postDate": "12/29/2025 23:11:17",
      "content": "<p>Finished looking at the rest of the output - I think for the shared notebook dates was all the missing issues.</p>",
      "rawMarkdown": "Finished looking at the rest of the output - I think for the shared notebook dates was all the missing issues.",
      "votes": null
    },
    {
      "id": "3383348",
      "postDate": "12/30/2025 00:02:58",
      "content": "<p>Thank you so much for your detailed help and for taking the time to help me, I really appreciate it! </p>\n<p>Here's the public link again for convenience:<br>\n<a href=\"https://www.kaggle.com/code/nctuan/image2biomass-afhn\" target=\"_blank\">https://www.kaggle.com/code/nctuan/image2biomass-afhn</a></p>\n<p>I'll fix this based on your suggestion and run the outputs again.</p>",
      "rawMarkdown": "Thank you so much for your detailed help and for taking the time to help me, I really appreciate it! \n\nHere's the public link again for convenience:  \nhttps://www.kaggle.com/code/nctuan/image2biomass-afhn\n\nI'll fix this based on your suggestion and run the outputs again.",
      "votes": null
    },
    {
      "id": "3383351",
      "postDate": "12/30/2025 00:34:13",
      "content": "<p>I've already fix that problem successfully. You save my life :)</p>",
      "rawMarkdown": "I've already fix that problem successfully. You save my life :)",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 3383225,
      "author_name": "pcjimmmy",
      "author_url": "",
      "post_date": "12/29/2025 16:51:21",
      "content": "<p>Not sure what your code is doing but I don't think you should be seeing any nan - when I have had them in my pivot I found mistakes in the creation of the data.</p>",
      "votes": null,
      "replies": [
        {
          "id": 3383229,
          "author_name": "nctuan",
          "author_url": "",
          "post_date": "12/29/2025 17:01:15",
          "content": "<p>Thank you for the quick reply!\nThat's interesting - so in your case, after properly constructing the pivot / wide table you ended up with (almost) no NaNs at all?</p>",
          "votes": null,
          "replies": [
            {
              "id": 3383234,
              "author_name": "pcjimmmy",
              "author_url": "",
              "post_date": "12/29/2025 17:05:04",
              "content": "<p>None - your table sound much larger than mine - dont think my pivot would have been big enough for 7140 nan's.</p>",
              "votes": null,
              "replies": [
                {
                  "id": 3383238,
                  "author_name": "pcjimmmy",
                  "author_url": "",
                  "post_date": "12/29/2025 17:09:04",
                  "content": "<p>You must be pivoting more than just the 5 labels - generating some type stats in the pivot - mean, etc. ??</p>\n<p>if you don't think you notebook is the magic winner you might make it public and it would be easier for folks to help.</p>\n<p>For this competition putting nan's in a 0.0 would likely be a mistake.  I tried finding some bare soil images that would have 0.0 for all the labels - just a few of them made my issues worse - my major model failure is big errors on the larger biomass images - always under predicting.  More 0.0 in the data would make the model even more inclined to have that error.</p>",
                  "votes": null,
                  "replies": [
                    {
                      "id": 3383239,
                      "author_name": "pcjimmmy",
                      "author_url": "",
                      "post_date": "12/29/2025 17:12:31",
                      "content": "<p>If your using one of the LLM like ChatGPT to help you code - show it the cell of code and copy part of the table where nan's are shown - than tell it your certain that nan's are wrong - whats my mistake.</p>\n<p>If you share your notebook that's probably the exact thing I will do to help you :)</p>",
                      "votes": null,
                      "replies": [
                        {
                          "id": 3383246,
                          "author_name": "nctuan",
                          "author_url": "",
                          "post_date": "12/29/2025 17:20:34",
                          "content": "<p>Thank you for your enthusiasm. I think we can consider on tomorrow 'cause it's 00:00 AM in VietNam :)</p>",
                          "votes": null,
                          "replies": [
                            {
                              "id": 3383247,
                              "author_name": "pcjimmmy",
                              "author_url": "",
                              "post_date": "12/29/2025 17:22:42",
                              "content": "<p>What the heck - most of my best thinking occurs on several cups of coffee after midnight !</p>\n<p>Good luck tomorrow !</p>",
                              "votes": null,
                              "replies": [
                                {
                                  "id": 3383331,
                                  "author_name": "pcjimmmy",
                                  "author_url": "",
                                  "post_date": "12/29/2025 22:24:40",
                                  "content": "<p>You should have linked your notebook into this discussion.  Just found it, and see that your initial lines of output show a number of missing values - will check it this evening - sometime before midnight :)   Maybe I can find what I think is an error.</p>",
                                  "votes": null,
                                  "replies": [
                                    {
                                      "id": 3383336,
                                      "author_name": "pcjimmmy",
                                      "author_url": "",
                                      "post_date": "12/29/2025 22:46:24",
                                      "content": "<p>First issue - the df_wide has NaT for the Sampling_date - this ends up in the nan count.  If the NaT had been real not sure what the best method would have been to fillna - never did missing dates that I can recall.</p>\n<p>Anyway - This line generates that error</p>\n<p><code>df_wide['Sampling_Date'] = pd.to_datetime(df_wide['Sampling_Date'], format='%m/%d/%Y', errors='coerce')</code></p>\n<p>The data supplied is in year month day format - coerce generates an NaT error since the format of the data does not match your \"requirement'.  I replaced it with this and about half the missing went away.</p>\n<p><code>df_wide['Sampling_Date'] = pd.to_datetime(df_wide['Sampling_Date'], dayfirst=True, errors='coerce')</code></p>\n<p>But for some reason there were still some NaT in the sample date column of wide.   So I replaced it with this attempt</p>\n<pre><code>df_wide['Sampling_Date'] = pd.to_datetime(df_wide['Sampling_Date'], format='%Y/%m/%d', errors='coerce')\n</code></pre>\n<p>That eliminated all the missing shown in the initial output</p>\n<p>Data Quality Summary:</p>\n<ul>\n<li>Missing values: 0</li>\n<li>States: ['Tas', 'NSW', 'WA', 'Vic']</li>\n<li>Date range: 2015-01-15 00:00:00 to 2015-11-10 00:00:00</li>\n</ul>\n<p>Still have not looked over all the output to see if this was the source of all your NAN's.</p>\n<p>Have written code (as a hobbist) in a bunch of programming languages over my lifetime.  </p>\n<p>Handling dates ALWAYS SUCKS.   </p>",
                                      "votes": null,
                                      "replies": [
                                        {
                                          "id": 3383341,
                                          "author_name": "pcjimmmy",
                                          "author_url": "",
                                          "post_date": "12/29/2025 23:11:17",
                                          "content": "<p>Finished looking at the rest of the output - I think for the shared notebook dates was all the missing issues.</p>",
                                          "votes": null,
                                          "replies": [
                                            {
                                              "id": 3383348,
                                              "author_name": "nctuan",
                                              "author_url": "",
                                              "post_date": "12/30/2025 00:02:58",
                                              "content": "<p>Thank you so much for your detailed help and for taking the time to help me, I really appreciate it! </p>\n<p>Here's the public link again for convenience:<br>\n<a href=\"https://www.kaggle.com/code/nctuan/image2biomass-afhn\" target=\"_blank\">https://www.kaggle.com/code/nctuan/image2biomass-afhn</a></p>\n<p>I'll fix this based on your suggestion and run the outputs again.</p>",
                                              "votes": null,
                                              "replies": [
                                                {
                                                  "id": 3383351,
                                                  "author_name": "nctuan",
                                                  "author_url": "",
                                                  "post_date": "12/30/2025 00:34:13",
                                                  "content": "<p>I've already fix that problem successfully. You save my life :)</p>",
                                                  "votes": null,
                                                  "replies": []
                                                }
                                              ]
                                            }
                                          ]
                                        }
                                      ]
                                    }
                                  ]
                                }
                              ]
                            }
                          ]
                        }
                      ]
                    }
                  ]
                }
              ]
            }
          ]
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "3383222": "Hi everyone,\nFirst of all, thank you to the hosts for a very interesting competition!\n\nWhile creating this wide-format table I found that 7140 missing values in pivot table. So filling with 0 might make domain sense for biomass (no observation = no biomass at that point in time?).\n\nStill, 7140 imputed zeros feel like a lot, and I'm worried this strong imputation choice is harming model convergence and generalization.\n\nI want to ask that:\n1. Do you think filling with 0.0 is conceptually okay in this setting, or does it introduce too much bias/noise?\n\n2. Have people tried alternative strategies that worked better? Some ideas I’m considering:\n    - Forward-fill / backward-fill per paddock (but this could leak future information if not done carefully with group-wise time ordering)\n    - Linear or spline interpolation per paddock/time-series\n    - Filling only early/late missing values with 0 and interpolating in the \"middle\" of each series\n    - Masking the missing values during loss computation (custom masked MSE / MAE) instead of imputing\n    - Dropping paddocks / time points with too many missing values\n    - Using models that natively handle missing data (e.g. certain GBDTs or time-series libraries that support NaNs)\n\n3. Did anyone who reached a decent leaderboard score keep the 0-filling strategy, or did you move away from it?\n\nVery grateful for any experiences, lessons learned, or suggestions.\n\nThanks in advance, and happy competing! \n\nBest regards,\nNguyen Cong Tuan",
    "3383225": "Not sure what your code is doing but I don't think you should be seeing any nan - when I have had them in my pivot I found mistakes in the creation of the data.",
    "3383229": "Thank you for the quick reply!\nThat's interesting - so in your case, after properly constructing the pivot / wide table you ended up with (almost) no NaNs at all?",
    "3383234": "None - your table sound much larger than mine - dont think my pivot would have been big enough for 7140 nan's.",
    "3383238": "You must be pivoting more than just the 5 labels - generating some type stats in the pivot - mean, etc. ??\n\nif you don't think you notebook is the magic winner you might make it public and it would be easier for folks to help.\n\nFor this competition putting nan's in a 0.0 would likely be a mistake.  I tried finding some bare soil images that would have 0.0 for all the labels - just a few of them made my issues worse - my major model failure is big errors on the larger biomass images - always under predicting.  More 0.0 in the data would make the model even more inclined to have that error.",
    "3383239": "If your using one of the LLM like ChatGPT to help you code - show it the cell of code and copy part of the table where nan's are shown - than tell it your certain that nan's are wrong - whats my mistake.\n\nIf you share your notebook that's probably the exact thing I will do to help you :)",
    "3383246": "Thank you for your enthusiasm. I think we can consider on tomorrow 'cause it's 00:00 AM in VietNam :)",
    "3383247": "What the heck - most of my best thinking occurs on several cups of coffee after midnight !\n\nGood luck tomorrow !",
    "3383331": "You should have linked your notebook into this discussion.  Just found it, and see that your initial lines of output show a number of missing values - will check it this evening - sometime before midnight :)   Maybe I can find what I think is an error.",
    "3383336": "First issue - the df_wide has NaT for the Sampling_date - this ends up in the nan count.  If the NaT had been real not sure what the best method would have been to fillna - never did missing dates that I can recall.\n\nAnyway - This line generates that error\n\n`df_wide['Sampling_Date'] = pd.to_datetime(df_wide['Sampling_Date'], format='%m/%d/%Y', errors='coerce')`\n\nThe data supplied is in year month day format - coerce generates an NaT error since the format of the data does not match your \"requirement'.  I replaced it with this and about half the missing went away.\n\n`df_wide['Sampling_Date'] = pd.to_datetime(df_wide['Sampling_Date'], dayfirst=True, errors='coerce')`\n\nBut for some reason there were still some NaT in the sample date column of wide.   So I replaced it with this attempt\n\n```python\ndf_wide['Sampling_Date'] = pd.to_datetime(df_wide['Sampling_Date'], format='%Y/%m/%d', errors='coerce')\n\n```\nThat eliminated all the missing shown in the initial output\n\nData Quality Summary:\n  - Missing values: 0\n  - States: ['Tas', 'NSW', 'WA', 'Vic']\n  - Date range: 2015-01-15 00:00:00 to 2015-11-10 00:00:00\n\nStill have not looked over all the output to see if this was the source of all your NAN's.\n\nHave written code (as a hobbist) in a bunch of programming languages over my lifetime.  \n\nHandling dates ALWAYS SUCKS.",
    "3383341": "Finished looking at the rest of the output - I think for the shared notebook dates was all the missing issues.",
    "3383348": "Thank you so much for your detailed help and for taking the time to help me, I really appreciate it! \n\nHere's the public link again for convenience:  \nhttps://www.kaggle.com/code/nctuan/image2biomass-afhn\n\nI'll fix this based on your suggestion and run the outputs again.",
    "3383351": "I've already fix that problem successfully. You save my life :)"
  },
  "source": "meta"
}