Data for "Early planting adaptation makes the coupled food-water system more sustainable under climate change"
Bibliographic record
Abstract
Data for submission "Early planting adaptation makes the coupled food-water system more sustainable under climate change" All data are in netcdf format and can be read in ncl, python, R code capacity. geo_em.d01.conus.corn: Domain setup file for the Noah-MP crop model in the US corn belt. Lat/lon location specified by "XLAT_M" and "XLONG_M" variable and corn planting area specified by "CROPTYPE" variable. Three zip files are uploaded containing data from model simulations and county-level yield and irrigation record: Yield_data.zip: yield data from model simulations (denoted by three scenarios, CTRL, PGW, TAVE), with irrigation (irr), and from USDA NASS (NASS). Irrigation_data.zip: Irrigation amount data from three scenarios (CTRL, PGW, TAVE for early planting), and from USGS water use record (2005 and 2010). TempPrecPET.zip: temperature and precipitation and potential evapotranspiration data (PET) for the CTRL and PGW climate scenarios.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.174 | 0.136 |
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".