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Record W2810626813 · doi:10.1111/jai.13748

Juvenile lake sturgeon monitoring and determinants of year‐class strength in the Rupert River, mid‐northern Québec, Canada

2018· article· en· W2810626813 on OpenAlexaffabout
Julie D'Amours, R. Dion

Bibliographic record

VenueJournal of Applied Ichthyology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsLake sturgeonWeirJuvenileFisherySturgeonHabitatAcipenserAbundance (ecology)BiologyEcologyStreamflowHydrology (agriculture)Fish <Actinopterygii>GeographyDrainage basin

Abstract

fetched live from OpenAlex

In 2007, Hydro-Québec began the construction of the Rupert Diversion in conjunction with the Eastmain-1A and Sarcelle powerhouses. The partial diversion of the Rupert River became operational in 2009. Mitigation measures to preserve lake sturgeon (Acipenser fulvescens) habitat downstream of the diversion include an instream flow, weir and spurs to maintain water levels, and fish passage channels and spawning grounds. An environmental follow-up was done in the reduced-flow section of the Rupert. The baseline status was established from 2007 to 2009 and follow-up studies were conducted from 2010 through 2012, and in 2014 and 2016. Besides presenting results from Hydro-Québec's environmental monitoring, analyses were performed to search for determinants of year-class strength. The results of the lake sturgeon monitoring activities indicate that the abundance of juveniles ≤8-year-old in the reduced flow section of the river remained similar or increased. Although larval production increased in post diversion conditions, cohort strength tended to decrease as did juvenile growth. Year-class strength was positively correlated with spring and summer flow. Also, a significant, strong negative correlation was found between estimated larval abundance and water temperature during larval drift.

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 score0.018
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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