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Record W119410102 · doi:10.2166/wqrj.2006.028

Cumulative Effects of Multiple Contaminants on Caged Fish

2006· article· en· W119410102 on OpenAlexaff
Jack F. Klaverkamp, Vince Palace, C. L. Baron, R. E. Evans, Kerry Wautier

Bibliographic record

VenueWater Quality Research Journal · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsUniversity of ManitobaGovernment of CanadaFisheries and Oceans Canada
Fundersnot available
KeywordsEffluentWastewaterFish <Actinopterygii>ContaminationVitellogeninEnvironmental chemistryGarbageFisheryToxicologyEnvironmental scienceChemistryBiologyWaste managementEnvironmental engineeringEcology

Abstract

fetched live from OpenAlex

Abstract Pearl dace (Semotilus margarita) were held in cages and exposed to mine effluents, municipal wastewater effluents, a combination of the two, or to the combination in addition to runoff from a garbage disposal facility. Fish exposed to mining effluents only had the lowest mean lengths and weights but highest concentrations of As, Ni and Hg and lowest Zn in their viscera. Fish exposed to municipal wastewater effluents only had the highest concentrations of Cd and metallothionein in their viscera. Histopathological analyses of gill and liver tissues revealed a higher incidence of lesions in fish exposed to municipal wastewater effluents. These fish also had the highest LSIs, condition factors and mean vitellogenin concentrations in plasma from males. Fish exposed near the garbage disposal site had the highest concentrations of Pb and Se in their viscera.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.062
GPT teacher head0.365
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2006
Admission routes1
Has abstractyes

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