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Record W2895986139 · doi:10.1088/1748-9326/aae93c

Deep groundwater quality in the southwestern United States

2018· article· en· W2895986139 on OpenAlexafffund
Mary Kang, James E. Ayars, Robert B. Jackson

Bibliographic record

VenueEnvironmental Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute of Food and Agriculture
KeywordsGroundwaterStructural basinIrrigationEnvironmental scienceAridWater resource managementUSableHydrology (agriculture)Resource (disambiguation)Groundwater rechargeGeologyAquiferEcology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.038
GPT teacher head0.285
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2018
Admission routes2
Has abstractyes

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