Using knowledge-guided machine learning to assess patterns of areal change in waterbodies across the contiguous U.S.: Data
Bibliographic record
Abstract
Data used for generating figures in the knowledge-guided machine learning manuscript and supplemental info by Wander et al. This repository contains three folders: Results: csv file with the final KGML groups for each waterbody. Latitude, longitude, RealSAT (Khandelwal et al., 2022) waterbody id, and HydroLAKES (Messager et al., 2016) waterbody id are provided. Code_data: data used for generating figures in the knowledge-guided machine learning manuscript and supplemental info by Wander et al. prism: data from PRISM dataset (Matsuura and Willmott) used for preliminary driver analysis in Wander et al. Khandelwal, A.; Karpatne, A.; Ravirathinam, P.; Ghosh, R.; Wei, Z.; Dugan, H.; Hanson, P. C.; Kumar, V. ReaLSAT, a global dataset of reservoir and lake surface area variations. Sci. Data 2022, 9, 356. https://doi.org/10.1038/s41597-022-01449-5 Messager, M. L.; Lehner, B.; Grill, G.; Nedeva, I.; Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 2016, 7, 1–11. https://doi.org/10.1038/ncomms13603Willmott, C. J.; Matsuura, K. Terrestrial Air Temperature and Precipitation: 1900-2014 Gridded Monthly Time Series: NOAA Physical Sciences Laboratory Terrestrial Air Temperature and Precipitation: 1900-2014 Gridded Monthly Time Series, 2015. https://psl.noaa.gov/data/gridded/data.UDel_AirT_Precip.html. (Accessed Jan 2022). Danielson, J. J.; Gesch, D. B. TEMIS -- GMTED2010 Elevation Data at Different Resolutions, 2011. https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-global-multi-resolution-terrain-elevation. (Accessed Jan 2022).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".