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Record W156804107

Inbreeding depression and segregation distortion in Chinook salmon: Conservation implications of genetic load

2012· article· en· W156804107 on OpenAlexaff
Kendra Komsa

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsInbreeding depressionChinook windInbreedingMatingBiologyFisheryZoologyFish <Actinopterygii>OncorhynchusDemographyPopulationSociology
DOInot available

Abstract

fetched live from OpenAlex

Strong evidence that shows inbreeding in many plants and animals can lead to detrimental effects at the level of the organism as well as the population. Inbreeding and its effects have been the focus of much attention in terms of conservation and captive rearing and breeding programs for many endangered species. Inbreeding depression (ID) is the fitness loss associated with individuals and populations that have experienced inbreeding. Surprisingly, ID studies within salmonids are not extensive. Here, I used inbred offspring of self-fertilized hermaphrodite parents to study the effects of ID in Chinook salmon ( Oncorhynchus tshawytscha ) at an extreme level of inbreeding. High levels of ID were found after a single generation of self-crossing, resulting in substantial fitness losses. The potential to purge genetic load from a population was shown to take place and the major genetic mechanism driving ID was due to expression of deleterious recessive alleles.

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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.207
Teacher spread0.192 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

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