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Anomalous stable hydrogen-oxygen isotope characteristics and water vapor sources of autumn precipitation in the Weihe River basin, Northwest China.

2023· article· en· W4386592880 on OpenAlexaff
Haoyan Wei, Yanwei Lu, Min Li, Peiyue Li, Wenqing Cheng, Bingcheng Si

Bibliographic record

VenuePubMed · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPrecipitationEnvironmental scienceδ18OMeteoric waterStable isotope ratioStructural basinAnomaly (physics)Isotopes of oxygenDrainage basinWater vaporClimatologyAtmospheric sciencesWater resourcesHydrology (agriculture)Physical geographyGeologyGeographyMeteorologyEcology

Abstract

fetched live from OpenAlex

O values. the variations of d-excess values and the slopes and intercepts of the meteoric water lines of autumn rain showed opposite trends. The main factor controlling autumn rain anomaly was not the local meteorological parameters, but the El Nino-Southern Oscillation and the Indian Ocean dipole events, which could explain 99% and 93% of the autumn rain isotopic variations, respectively. These coupling phenomena affected water vapor transport intensity of the marine air mass to the northwest inland, which determined autumn rainfall amount and the stable hydrogen-oxygen isotope composition. Our results would be helpful for improving the understanding of autumn rain anomalies in West China, and provide basic data and theoretical support for regional hydrological model building, would thereby better serve water resources management and disaster prevention and reduction.

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)0.000
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 score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

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

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 head0.010
GPT teacher head0.172
Teacher spread0.162 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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