A comprehensive framework for integrating lake hypsography and function on a global scale
Bibliographic record
Abstract
Data.zip: 1. Lake morphometry data: global lakes map data with area, predicted Zmax, Zmean, a lake morphometry dataset used to build the random forests model. 2. lake_bathymetry_and_hypsography, a list of lakes with n ERA5 data and a list of lakes >400 km2 with morphometry data. 3. resampled_Zmax_and_q: resampled Zmax and q based on the uncertainty in their prediction (Table S3) 4. Sims_subset: a data subset to carry out model simulations 5. Uberlakes_Climatic_predicted: data containing climatic region composite/uberlakes hypsography and features based on predicted Zmax and q. 6. Uberlakes_Climatic_predicted: data containing climatic region composite/uberlakes hypsography and features based on resampled Zmax and q. 7. Uberlakes_Global_predicted: data containing global composite/uberlakes hypsography and features based on predicted Zmax and q. 8. Uberlakes_Global_resampled: data containing global composite/uberlakes hypsography and features based on resampled Zmax and q. 9. Uncertainty_Stats: data containg uberlakes uncertainty statistics 10. Fig_S9_individual_lake_hypsography_predicted_relative: hypsography data for Figure S9a 11. Fig_S9_individual_lake_hypsography_resampled_relative: hypsography data data for Figure S9b 12. dynamic simulation_results: data from dynamic simulations at the regions and grid level, with table summary files, daily (dUberlake) and profile data(Uberlake). lake_hypsography_code.zip: contains the code for this study.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.014 |
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".