MétaCan
Menu
Back to cohort
Record W4313829478 · doi:10.1038/s43247-022-00650-z

Shift in groundwater recharge of the Bengal Basin from rainfall to surface water

2023· article· en· W4313829478 on OpenAlexfundno aff
Yusuf Jameel, Mason Stahl, Holly A. Michael, Benjamín C. Bostick, M. S. Steckler, Peter Schlösser, Alexander van Geen, Charles F. Harvey

Bibliographic record

VenueCommunications Earth & Environment · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsnot available
FundersNatural Resources CanadaAbdul Latif Jameel Water and Food Systems Lab, Massachusetts Institute of TechnologyU.S. Geological Survey
KeywordsGroundwater rechargeGroundwaterHydrology (agriculture)Depression-focused rechargeEnvironmental scienceSurface waterInfiltration (HVAC)MonsoonDry seasonStructural basinPrecipitationGeologyAquiferGeographyGeomorphologyClimatologyEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract Groundwater supports agriculture and provides domestic water for over 250 million people in the Bengal Basin. Here we investigate the source of groundwater recharge using over 2500 stable water isotope measurements from the region. We employ a Monte Carlo statistical analysis to find distributions of possible components of recharge by accounting for the variability of isotope ratios in each of the possible recharge sources. We find that groundwater recharge sources have shifted in the last decades with a ~50% increase in recharge from stagnant surface water bodies (mostly during the latter part of the dry season) and a relative decrease in contribution from direct infiltration of precipitation (which occurs mostly in the early monsoon). We attribute this shift to an increase in standing water in irrigated rice fields and ponds, and an increase in the downward hydraulic gradient during the dry season driven by pumping.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.023
GPT teacher head0.209
Teacher spread0.185 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations24
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCommunications Earth & EnvironmentSame topicGroundwater and Isotope GeochemistryFrench-language works237,207