Data for 'Sustainability of Irrigation and Streamflow in the Western US'
Bibliographic record
Abstract
Materials required to reproduce the analysis reported in the paper. Dec 17, 2025 Addition: The final IrrMapper training dataset from which points were extracted, Earth Engine raster information was exported, training tables built, and raster inference executed using https://github.com/dgketchum/EEMapper. Each land type has a shapefile (uncultivated, dryland agriculture, irrigated agriculture with year of irrigation, and wetlands), each of which has tens of thousands of polygons, providing a land cover estimate over hundreds of thousands of square kilometers. Notes: There are shapefiles for 'dryland' (unirrigated crops) and 'fallow' (equipped but not irrigated for the year in the attribute table). We combine these into a single, unirrigated crops class for IrrMapper. Projection: North_America_Albers_Equal_Area_ConicPropertiesUnits: metersStatic (relies on a datum which is plate-fixed)Celestial body: EarthMethod: Albers Equal AreaWKTPROJCRS["North_America_Albers_Equal_Area_Conic", BASEGEOGCRS["NAD83", DATUM["North American Datum 1983", ELLIPSOID["GRS 1980",6378137,298.257222101, LENGTHUNIT["metre",1]]], PRIMEM["Greenwich",0, ANGLEUNIT["Degree",0.0174532925199433]]], CONVERSION["North_America_Albers_Equal_Area_Conic", METHOD["Albers Equal Area", ID["EPSG",9822]], PARAMETER["Latitude of false origin",40, ANGLEUNIT["Degree",0.0174532925199433], ID["EPSG",8821]], PARAMETER["Longitude of false origin",-96, ANGLEUNIT["Degree",0.0174532925199433], ID["EPSG",8822]], PARAMETER["Latitude of 1st standard parallel",20, ANGLEUNIT["Degree",0.0174532925199433], ID["EPSG",8823]], PARAMETER["Latitude of 2nd standard parallel",60, ANGLEUNIT["Degree",0.0174532925199433], ID["EPSG",8824]], PARAMETER["Easting at false origin",0, LENGTHUNIT["metre",1], ID["EPSG",8826]], PARAMETER["Northing at false origin",0, LENGTHUNIT["metre",1], ID["EPSG",8827]]], CS[Cartesian,2], AXIS["(E)",east, ORDER[1], LENGTHUNIT["metre",1]], AXIS["(N)",north, ORDER[2], LENGTHUNIT["metre",1]], USAGE[ SCOPE["Not known."], AREA["North America - onshore and offshore: Canada - Alberta; British Columbia; Manitoba; New Brunswick; Newfoundland and Labrador; Northwest Territories; Nova Scotia; Nunavut; Ontario; Prince Edward Island; Quebec; Saskatchewan; Yukon. United States (USA) - Alabama; Alaska (mainland); Arizona; Arkansas; California; Colorado; Connecticut; Delaware; Florida; Georgia; Idaho; Illinois; Indiana; Iowa; Kansas; Kentucky; Louisiana; Maine; Maryland; Massachusetts; Michigan; Minnesota; Mississippi; Missouri; Montana; Nebraska; Nevada; New Hampshire; New Jersey; New Mexico; New York; North Carolina; North Dakota; Ohio; Oklahoma; Oregon; Pennsylvania; Rhode Island; South Carolina; South Dakota; Tennessee; Texas; Utah; Vermont; Virginia; Washington; West Virginia; Wisconsin; Wyoming."], BBOX[23.81,-172.54,86.46,-47.74]], ID["ESRI",102008]]Proj4+proj=aea +lat_0=40 +lon_0=-96 +lat_1=20 +lat_2=60 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defsExtent-172.54, 23.81, -47.74, 86.46
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.041 |
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".