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Record W4283784821 · doi:10.1016/j.heliyon.2022.e09848

Water quality index assessment methods for surface water: A case study of the Citarum River in Indonesia

2022· article· en· W4283784821 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

VenueHeliyon · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
FundersInstitut Teknologi Bandung
KeywordsSurface waterWater qualityEnvironmental scienceIndex (typography)Water resource managementEnvironmental engineeringBiology

Abstract

fetched live from OpenAlex

Water quality index (WQI) can express overall water quality status in a single term. As such, the application of daily WQI assessment should help the general public be more aware of the condition of the surface water around them. As the longest and biggest river in the West Java Province, the Citarum River plays an important role in the life of the community and ecosystem around it. Therefore, this research evaluated which WQI assessment method was best suited for determining the Citarum River's water quality. We utilized West Java Province monitoring data collected from four monitoring stations along the Upstream Citarum. The WQI was calculated using the National Sanitation Foundation WQI (NSF WQI), Canadian Council of Ministers of the Environment WQI (CCME WQI), and Oregon Water Quality Index (OWQI) assessment methods. Nine years of monitoring data were grouped and analyzed according to wet vs. dry months, wet vs. dry years, monitoring station, and year. Using the NSF WQI assessment method, the Citarum River obtained a 'Fair' and 'Bad' water quality grade with WQI ranging between 38.212 and 60.903 during dry months, 49.089 and 62.348 during wet months, 42.935 and 65.696 during dry years, and 39.002 and 58.898 during wet years. The data ranged from 41.458 and 61.206 from each monitoring station, and between 35.920 and 58.713 for the data from each monitoring year. The CCME WQI assessment method showed that the Citarum River had 'Fair', 'Marginal', and 'Bad' water quality with WQI ranging between 12.683 and 31.503 during dry months, 21.231 and 33.127 during wet months, 12.683 and 31.503 during dry years, 12.134 and 28.748 during wet years, 13.621 and 30.569 for the data from each monitoring station, and 13.219 and 68.808 for the data from each monitoring year. The OWQI assessment method gave the Citarum River a 'Very Bad' water quality rating with WQI ranging between 11.528 and 18.827 during dry months, 13.898 and 24.563 during wet months, 11.528 and 25.782 during dry years, 11.528 and 15.997 during wet years, 11.528 and 18.842 for each monitoring station, and 11.523 and 16.528 for the data from each monitoring year. Based on these results and the collated advantages and disadvantages of each method, the NSF WQI assessment method was deemed to be the best for determining the Citarum River's water 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)0.004
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 score0.038
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.393
Teacher spread0.334 · 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