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Water Quality Deterioration of Jinjang River, Kuala Lumpur: Urban Risk Case Water Pollution

2014· article· en· W2337734937 on OpenAlex
Shamin Aizat Abdul Rashid, Muhammad Barzani Gasim, Mohd Ekhwan Toriman, Hafizan Juahir, Mohd Khairul Amri Kamarudin, Azman Azid, Nor Azlina Abdul Aziz

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.

venuePublished in a venue whose home country is Canada.
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

VenueArab world geographer · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsWater qualityEnvironmental scienceHydrology (agriculture)Fecal coliformBankRiver pollutionPollutionKuala lumpurWater pollutionWater resource managementGeographyEcologyBiologyGeology

Abstract

fetched live from OpenAlex

Jinjang River is a branch of the Klang River, which today suffers from a decline in water quality resulting from agricultural and development activities. A study on the water quality of Jinjang River was conducted in both June and October 2011. The purposes of the study were to determine the water quality of Jinjang River based on physicochemical and biological parameters and to classify the Jinjang River based on National Water Quality Standards (NWQS) and the Water Quality Index (WQI). A total of five sampling stations were selected along the river; two stations (S1 and S2) represented the upstream region and another three stations (S3, S4, and S5) represented the downstream region of the river. Fourteen water-quality parameters were selected. As a result of the analysis, Jinjang River was categorized as a slightly polluted river (WQI) and was classified as Class III. The result, compared with the NWQS, showed that most of the water-quality parameters studied ranged from Class I to Class IV, except for biological parameters (Escherichia coli), which were classified as Class V. This indicates that the river was extremely contaminated with fecal coliform bacteria (E. coli).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.998

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)0.0030.001

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 head0.014
GPT teacher head0.257
Teacher spread0.243 · 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