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Record W4407301254 · doi:10.3354/esr01395

Population viability analysis of the endangered copper redhorse Moxostoma hubbsi

2025· article· en· W4407301254 on OpenAlexaffabout
N Bannester-Marchand, Nathalie Vachon, Nicholas E. Mandrak, F. Guillaume Blanchet

Bibliographic record

VenueEndangered Species Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsThe Scarborough HospitalUniversity of TorontoMinistère des Ressources naturelles et des ForêtsUniversité de Sherbrooke
Fundersnot available
KeywordsEndangered speciesFisheryPopulation viability analysisPopulationGeographyZoologyBiologyEcologyHabitatMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Studying population dynamics of freshwater fishes facing extinction is becoming increasingly important to better understand how to protect them in our fast-changing environment. In this study, our objective was to better understand copper redhorse Moxostoma hubbsi (an endangered fish species only found in Quebec, Canada) population dynamics and the effect of stocking to quantify its risk of extinction. We used stochastic population dynamics models based on current survival and reproduction knowledge of copper redhorse. Although our models suggested that extinction probabilities were low, they show that the population is sensitive to random variability and that it is highly likely for copper redhorse population abundance to become low (<250 individuals), making them vulnerable to genetic impoverishment. Also, the low survival of the younger age-classes made them of greater concern for conservation. Our model shows larvae and fry stocked in the Richelieu River contribute to maintaining the copper redhorse population; however, it also emphasizes the importance of diversifying the current program by stocking 1 yr old individuals to increase population size and help prevent extinction.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.566
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.054
GPT teacher head0.372
Teacher spread0.318 · 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 designBench or experimental
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

Citations1
Published2025
Admission routes2
Has abstractyes

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