MétaCan
Menu
Back to cohort
Record W2009833652 · doi:10.5539/jsd.v3n2p210

An Assessment of the Effect of Industrial and Sewage Effluent on Aquatic Invertebrates: A Case Study of A Southern Urban Stream, Zimbabwe

2010· article· en· W2009833652 on OpenAlexvenueno aff
Trevor Dube, Caston Makaka, Zwelabo Sibanda

Bibliographic record

VenueJournal of Sustainable Development · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsEffluentInvertebrateEnvironmental scienceBenthic zoneWater qualityPollutionPollutantOdonataSewageWater pollutionEcologyHydrology (agriculture)Environmental chemistryBiologyEnvironmental engineeringChemistry

Abstract

fetched live from OpenAlex

The impact of industrial effluent discharged in Mazai stream was assessed through physical-chemical parameters and also by bio-monitoring of benthic macro-invertebrates. Samples were collected at three sites, one before the effluent discharge point into the stream (site 3) and two sites which were located downstream after the discharge points (sites 1 and 2). High levels of chemical pollutants were recorded at sites 1 and 2 (ZINWA red category) whereas site 3 (reference site) consisted of relatively clean water (ZINWA blue category). This was confirmed by the biological evaluation process. The SASS4 scores at sites 1 and 2 indicated a deterioration of water quality while site 3 there was good water quality with high species diversity. Detrended correspondence analysis (DCA) showed that pollution sensitive taxa such as Hemiptera, Trichoptera, Coleoptera and Odonata were dominant at site 3 whilst the other sites were dominated by pollution tolerant species such Chironomids. Continuous discharge of effluent could lead to extreme degradation of Mazai stream hence loss of biodiversity of macro-invertebrates.

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

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.443

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 communication0.0000.000
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 head0.015
GPT teacher head0.285
Teacher spread0.270 · 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

Machine predicted; a candidate call from one teacher head, not a consensus.

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

Citations15
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sustainable DevelopmentSame topicWater Quality and Pollution AssessmentFrench-language works237,207