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Record W3164463007 · doi:10.13227/j.hjkx.202011068

[Hydrochemistry and Its Controlling Factors and Water Quality Assessment of Shallow Groundwater in the Weihe and Jinghe River Catchments].

2021· article· en· W3164463007 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Quality and Pollution
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGroundwaterHydrology (agriculture)Environmental scienceWater qualityGeologyGeotechnical engineeringEcologyBiology

Abstract

fetched live from OpenAlex

-Na-K (accounted for 32.5%), respectively. The hydrochemistry of the Weihe and Jinghe River catchments is mainly controlled by rock weathering, primarily silicate weathering. Moreover, the groundwater chemistry in the research area is affected by mining and chemical fertilizer application for agriculture. Furthermore, the hydrochemistry of the Weihe River catchment is affected by cation exchange, although this was not obvious in some regions of the Jinghe River catchment. The overall groundwater quality of the two catchments was good, with the Jinghe River water quality being better than in the Weihe River catchment. Based on SSP, SAR, and PI, the groundwater in some parts of the study area cannot be directly used for irrigation as this would result in salinization and, thus, inhibit plant growth. Overall, the groundwater quality in the south of the study area is better than in the north, and is better in the Jinghe River catchment than in the Weihe River catchment according to these three indicators. This study provides a basis for the sustainable development of two catchments, providing baseline data for groundwater quality management.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.031
GPT teacher head0.254
Teacher spread0.223 · 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