Bibliographic record
Abstract
Declining groundwater levels are common in parts of the western US, but their impact on the ability of wells to pump groundwater is not known. Here we collate groundwater well records for the western United States and present the recorded locations, depths, and purposes of more than two million groundwater wells constructed between 1950 and 2015. We then use the well records to estimate the percentage of wells that were dry during the years 2013–2015. During the two year period, dry wells were concentrated in rural areas with high agricultural productivity, such as parts of the California Central Valley and the High Plains. Our results support anecdotal evidence that wells used for domestic purposes are more susceptible to drying than wells used for agricultural purposes throughout California's Central Valley because the former tend to be shallower. However, this is not the case in all regions. Our findings suggest that declining groundwater levels are threatening drinking water reliability and agricultural productivity, and consequently, have key implications for both domestic and agricultural water security. Ongoing reductions to groundwater storage are drying groundwater wells in the western US, and this manifestation of water scarcity warrants innovative groundwater management transcending status quos.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".