MétaCan
Menu
Back to cohort
Record W3201663563 · doi:10.22401/anjs.24.3.10

Water Quality Assessment of Paper Mills Effluent Discharge Areas

2021· article· en· W3201663563 on OpenAlexaboutno aff
Md. Shakilur Zaman Shakil, M. G. Mostafa

Bibliographic record

VenueAl-Nahrain Journal of Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsEffluentEnvironmental scienceWater qualitySurface waterGroundwaterPollutionEnvironmental engineeringWater pollutionHydrology (agriculture)Environmental chemistryChemistryEngineeringEcology

Abstract

fetched live from OpenAlex

The study attempted to assess the water quality around paper mill effluents discharge areas. Several physicochemical parameters and the Canadian Council of Ministers of the Environment (CCME) Water Quality Index (WQI) were considered to determine the pollution level of surface and groundwater in the selected paper mills areas located in Saidpur, Gobindaganj, and Dupchanchia Upazilas of Bangladesh. Physicochemical characterization of the surface water around the paper mills areas showed that the concentration of EC, TSS, BOD5, COD, phenols, NO3−-N, and K+were exceeded the surface water standard, whereas the DO level ranged from 1.63 to 3.5 were found below the Environmental Conservation Rules (ECR), 1997 standard. Besides, the BOD, COD, and Mn ion concentrations of groundwater exceeded the drinking water standard. In most sampling sites, the WQI of the surface water showed ‘marginal’ category, and the groundwater quality showed 'fair' category. The study observed that the toxic effluents discharged from the paper mills caused harm to the aquatic ecosystem.

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.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.006
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.033
GPT teacher head0.342
Teacher spread0.310 · 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

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAl-Nahrain Journal of ScienceSame topicWater Quality and Pollution AssessmentFrench-language works237,207