Deep groundwater quality in the southwestern United States
Bibliographic record
Abstract
Groundwater demands are growing in many arid regions, and the use of non-traditional water resources, especially during extreme droughts, is increasingly common. One non-traditional resource is deep groundwater, which we define from ∼150 m to several kilometers or more deep. We analyze 41 081 data points from 17 basins in the southwestern United States (US) to estimate the distribution of fresh and usable deep groundwater for potential human consumption and irrigation. We find the Great Basin to have the largest percentages of fresh and usable deep groundwater with 88%, 96%, and 98% of the total dissolved solids (TDS) concentrations less than 1000 ppm, 3000 ppm and 10 000 ppm respectively. Seven out of the 17 southwestern basins indicate the presence of substantial quantities of usable deep groundwater (<10 000 ppm TDS). We also find that the Great Basin and the Central Valley of California have 64% and 36%, respectively, of deep groundwater with sufficiently low toxic (Na, Cl, and B) and trace element concentrations for irrigation use without treatment, with greater percentages available for more tolerant crops. Given the potentially large deep fresh and usable groundwater volumes across the southwestern US, it is important to characterize the resource and protect it for potential use in decades and centuries to come.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".