MétaCan
Menu
Back to cohort

Fuzzy Comprehensive Evaluation on North-China Groundwater Quality

2012· article· en· W2067506141 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

VenueAdvanced materials research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Quality and Pollution
Canadian institutionsCanadian Association of General Surgeons
FundersNational Key Research and Development Program of China
KeywordsFuzzy logicGroundwaterNormalization (sociology)Evaluation methodsGroundwater resourcesChinaEnvironmental scienceQuality (philosophy)Water resource managementEnvironmental engineeringComputer scienceHydrology (agriculture)GeologyReliability engineeringEngineeringGeographyGeotechnical engineeringAquiferArtificial intelligence

Abstract

fetched live from OpenAlex

The representative pollution factors are selected to undergo the single-factor membership grade evaluation and weight normalization treatment, and then fuzzy comprehensive evaluation method is employed to analyze the groundwater quality in a particular region in North-China Plain. The weight of each factor is determined as per the measured density value, thus guaranteeing objective and accurate evaluation results, which provide scientific grounds for the water resources planning and treatment. Therefore, the fuzzy comprehensive evaluation method is a scientific means of efficiently and accurately evaluating the groundwater quality.

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)0.003
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.007

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 head0.157
GPT teacher head0.435
Teacher spread0.277 · 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