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Record W3199375601 · doi:10.24908/iqurcp.9174

It’s Of(fish)cial: Pulp Mill Effluent Can Alter Reproductive Cycles of Ontario Fish

2018· article· en· W3199375601 on OpenAlexvenueaboutno aff
Julie Hovey, Robert Fillier, Christopher Heysel, Laura Lintott, Andrew Lue

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEffluentPaper millPulp (tooth)Pulp millBiologyPollutantPulp and paper industryEnvironmental scienceToxicologyEcologyEnvironmental engineering

Abstract

fetched live from OpenAlex

Pulp and paper mills are of economic importance in Canada, however their effluent waste is being channeled into water bodies and causing a variety of negative effects in aquatic ecosystems. Pulp mill effluents are chemical compounds which are oxygen consuming, persistent, and toxic, and have the capacity to mimic physiological compounds. A review of current literature on pulp mill effluent reveals that these chemicals can mimic the reproductive hormones of fish, thereby having effects on local fish reproductive cycles. These reproductive alterations include decreased steroidogenesis, reduced gonad size, and altered expression of secondary sex characteristics that together can affect the health of wild fish populations. However, there has been considerable variation found in the effects of pulp mill effluent based on chemical composition of the pollutants, and the sex, species, and exposure duration of the affected fish. Biotreatment has been considered as a viable option for reducing the impact of effluent on fish reproduction. We suggest that alterations in reproductive cycles can have downstream effects through trophic cascades which in turn may have widespread effects on community structure. Future research should include analysis of long term consequences on multiple species in affected ecosystems, as well as further study on the use of biotreatment to reduce the impact of effluent.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.733

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.075
GPT teacher head0.336
Teacher spread0.261 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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