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I’m confused, based on titles, about how lake metadata is distributed between 1 and 3 updated the titles to make it clear 3_ is for PB configs and not metadata.
2332 lakes = 1882 + 305 + 145. Where is this partitioning specified? I see type in lake_metadata.csv, but it only gives “source” or “target” and doesn’t distinguish between 305 and extended targets added a "ext_target" to the type column, which is what is used for the 1882. There are now 305 with "target", 145 with "source", and 1882 with "ext_target".
Why include FIPS/NIST in the Places? not sure...I think this is in meddle or the way the metadata is rendered by SB?
Process date of 7/6/2019 seems unlikely, unless we’re counting from nearer the start of the project on purpose updated
[ ] 1 lake info
In a lot of the Range Domain Max/Min values are NA not sure I can update all of these in time for the pre-print deadline, but would like to
Obs_depth_mean_frac is a fraction but has a maximum of 2.12 this is explained in the metadata.
I’d say “even though…” instead of “even while appears in the data release” updated
[ ] 2 temp obs
Prmnn_I is quite cryptic added text that explains this is an NHD attribute
[ ] 3 model configs
Consider specifying in the title that this child item contains configs for the process-based models only updated
Entity type definition for pb0: what is “expert-chosen default”? they weren’t just defaults for pb0, were they? They were the final parameters, right? updated to "lake-specific parameterization"
[ ] 4 model inputs
Title of item (“meteorological inputs and ice flags”) does not suggest that metamodel metafeatures will be provided in this item, but they are. changed to just "Model inputs"
Range domain Mins/Maxes are NA for several variables would like to update this too, but not much time before pre-print
Prmnn_I is still cryptic updated same as other
Diff_lathrop_strat: does 1 mean source=TRUE, target=FALSE? Or the other way around? Looking for clear statement of conversion from binary to integer
Why wouldn’t mtl_input_metafeatures.csv contain source-target info for all source-target pairs (including those for the extra 1882)? saving this as a question for Jared
[ ] 5 model prediction data
Can you title this “model predictions” instead of “model prediction data”? I think calling it data is misleading, even though this is a “data” release and all updated
Abstract seems to be for a different project – Minnesota and Wisconsin only, DL models as well as PB and PGDL and PB0. fixed
Native Data Set Environment: nice that we’ve at least nodded to the UM supercomputing system in this metadata file, but it mentions DL predictions and doesn’t mention MTL metamodel predictions. Added python env
I think here you could have one entity type for all of the model predictions and then template the model. But it’s OK as-is. keeping
Pbmtl_predictions_09 is missing known issue, since none of the 305 targets are in group 09.
[ ] 6 model evaluation
Could all_MTL_RMSE_predictions.csv also report predictions for the additional 1882 lakes? saving as Jared question too
I’d suggest only reporting the top 9 (predicted) sources for each extended target, and populating the predicted and actual RMSEs and predicted rank for PGDL-MTL and PGDL-MTL9 sources, and using NAs for actual/pred_pb_mtl_rmse/rank and actual_pgdl_mtl_rank saving this as a question for later due to limited time
The pgmtl*_evaluation.csv files report targets and RMSEs for all 2188, so that’s good. all good
It also appears that preditions matched to obs for PGDL-MTL[9] include the extended targets, rigth? yes
It also appears that the raw predictions for PGDL-MTL[9] include extended targets? yes
Entity Type Definition – could note that the file contains not just MTL (metamodel?) predictions but also actual results for performance of source models applied to targets updated this text
Metadata file doesn’t yet describe the xx_matched_to_observations.zip files or xx_evaluation.csv files or sparse_PGDL_vs_PGDL-MTL_rmse.csv file updated now
In sparse_PGDL_vs_PGDL-MTL_rmse.csv, it’s weird that RMSE cells for which no model was possible are filled with 0 instead of NA filtered out these rows instead of using NA (or 0). Thanks for pointing that out
The text was updated successfully, but these errors were encountered:
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[ ] Overall
I’m confused, based on titles, about how lake metadata is distributed between 1 and 3updated the titles to make it clear 3_ is for PB configs and not metadata.2332 lakes = 1882 + 305 + 145. Where is this partitioning specified? I see type in lake_metadata.csv, but it only gives “source” or “target” and doesn’t distinguish between 305 and extended targetsadded a "ext_target" to thetype
column, which is what is used for the 1882. There are now 305 with "target", 145 with "source", and 1882 with "ext_target".Process date of 7/6/2019 seems unlikely, unless we’re counting from nearer the start of the project on purposeupdated[ ] 1 lake info
Obs_depth_mean_frac is a fraction but has a maximum of 2.12this is explained in the metadata.I’d say “even though…” instead of “even while appears in the data release”updated[ ] 2 temp obs
Prmnn_I is quite crypticadded text that explains this is an NHD attribute[ ] 3 model configs
Consider specifying in the title that this child item contains configs for the process-based models onlyupdatedEntity type definition for pb0: what is “expert-chosen default”? they weren’t just defaults for pb0, were they? They were the final parameters, right?updated to "lake-specific parameterization"[ ] 4 model inputs
Title of item (“meteorological inputs and ice flags”) does not suggest that metamodel metafeatures will be provided in this item, but they are.changed to just "Model inputs"Prmnn_I is still crypticupdated same as other[ ] 5 model prediction data
Can you title this “model predictions” instead of “model prediction data”? I think calling it data is misleading, even though this is a “data” release and allupdatedAbstract seems to be for a different project – Minnesota and Wisconsin only, DL models as well as PB and PGDL and PB0.fixedNative Data Set Environment: nice that we’ve at least nodded to the UM supercomputing system in this metadata file, but it mentions DL predictions and doesn’t mention MTL metamodel predictions.Added python envI think here you could have one entity type for all of the model predictions and then template the model. But it’s OK as-is.keepingPbmtl_predictions_09 is missingknown issue, since none of the 305 targets are in group 09.[ ] 6 model evaluation
The pgmtl*_evaluation.csv files report targets and RMSEs for all 2188, so that’s good.all goodIt also appears that preditions matched to obs for PGDL-MTL[9] include the extended targets, rigth?yesIt also appears that the raw predictions for PGDL-MTL[9] include extended targets?yesEntity Type Definition – could note that the file contains not just MTL (metamodel?) predictions but also actual results for performance of source models applied to targetsupdated this textMetadata file doesn’t yet describe the xx_matched_to_observations.zip files or xx_evaluation.csv files or sparse_PGDL_vs_PGDL-MTL_rmse.csv fileupdated nowIn sparse_PGDL_vs_PGDL-MTL_rmse.csv, it’s weird that RMSE cells for which no model was possible are filled with 0 instead of NAfiltered out these rows instead of using NA (or 0). Thanks for pointing that outThe text was updated successfully, but these errors were encountered: