Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): Streamflow observations, forcing data and geospatial data for hydrologic studies across North America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This resource contains the CAMELS-SPAT data set. CAMELS-SPAT provides data that can support hydrologic modeling and analysis for 1426 streamflow measurement stations located across the United States and Canada. The area upstream of each station has been divided into various subbasins. The provided data include: (1) shapefiles outlining the location of each basin and its subbasins, (2) streamflow observations at daily and hourly resolution at the outlet of each basin, (3) meteorological data from 4 different data sets (RDRS, EM-Earth, ERA5, Daymet), at their native gridded resolution as well as averaged to the basin and subbasin level, (4) geospatial data from 11 different data at their native gridded resolution, and (5) statistical summaries (i.e. catchment attributes) calculated from the streamflow, meteorological and geospatial data at the basin and subbasin level. Data set structure is described in the README found in this repository. Data set development is described in Knoben et al (to be submitted). When using the CAMELS-SPAT data, please follow the attribution guidelines provided in Section 6 in this paper (briefly, individual attribution of any data set included in CAMELS-SPAT is requested if this data is used). BibTeX entries for the individual data sources aggregated in CAMELS-SPAT are provided in the citation.bib file found in this repository. Reference: Knoben, W. J. M., Keshavarz, K., Torres-Rojas, L., Thébault, C., Chaney, N. W., Pietroniro, A. & Clark, M. P. (to be submitted). Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): Streamflow observations, forcing data and geospatial data for hydrologic studies across North America.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.411 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it