CAMELS-spat: catchment data for spatially distributed large-sample hydrology
Bibliographic record
Abstract
The recent publication of large-sample datasets for hydrologic modeling and analysis has led to a revival of comparative hydrology. The “CAMELS” branch of these datasets currently provide catchment attributes and meteorological time series for basins located in the United States, Chile, Brazil, Australia and Great-Britain, with a dataset for France under development. A key characteristic of these datasets is that information is provided as catchment-averaged data; i.e. each catchment is treated as a lumped entity with no spatial variability. Some progress is being made to extend large-sample hydrology to include spatially distributed data, most notably by the recent LamaH dataset which covers part of Central Europe. Here we present progress on developing a continental domain dataset for large-sample hydrology intended for spatially distributed modeling and analysis. Our domain covers the United States and Canada, expanding both geographically and climatically on the region covered by the LamaH dataset. We focus mostly on relatively undisturbed headwater catchments, because accurate data on water management policies and infrastructure can be difficult to obtain. Our aim is to provide the necessary data for process-based modeling and analysis at a sub-daily temporal resolution.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.015 |
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".