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Record W2970733504 · doi:10.1016/j.dib.2019.104462

Dataset on water quality characteristics of a hill stream in Bhaderwah, Jammu and Kashmir

2019· article· en· W2970733504 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueData in Brief · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsnot available
FundersUniversity of Jammu
KeywordsTributaryTurbidityWater qualityEnvironmental sciencePollutionHydrology (agriculture)Channel (broadcasting)GeographyEcologyGeologyBiologyCartography

Abstract

fetched live from OpenAlex

The article summarises the data on water quality characteristics of Neeru stream analysed monthly for two years on a seasonal basis averaged to one year. Twenty-five water samples were collected and analysed to understand the quality of water based on index parameters. The data indicates marked variations in the concentration of most constituents mostly in the urban and suburban sections of the stream. The values for Canadian Water Quality Index (CWQI) were within acceptable range except for turbidity and nickel. The tributaries T-1, T-3 (III) and T-4 flowing close to urban settlement revealed relatively high levels of pollution (WQI:85-90), while T-3 (II) bisecting Bhaderwah town was heavily polluted (WQI:82.97). The main water channel (MC-1 to MC-10) with moderate to heavy pollution load in the middle and lower sections revealed reasonably good water quality (WQI 90-95).

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 score0.424
Threshold uncertainty score0.997

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 head0.062
GPT teacher head0.358
Teacher spread0.295 · 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