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Record W2043124872 · doi:10.1139/f09-168

Prospects for a genetic management strategy to control Gyrodactylus salaris infection in wild Atlantic salmon (Salmo salar) stocks

2009· article· en· W2043124872 on OpenAlexvenueno aff
Ragnar Salte, Hans B. Bentsen, Thomas Moen, Smita Tripathy, Tor A. Bakke, Jørgen Ødegård, Stig W. Omholt, Lars P. Hansen

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsSalmoBiologyHeritabilityTraitSalmonidaeZoologyGenetic variationParasite hostingEcologyFisheryFish <Actinopterygii>GeneticsGene

Abstract

fetched live from OpenAlex

We estimated additive genetic variation and heritability of survival after Gyrodactylus salaris infection from survival records in a pedigreed family material of wild Atlantic salmon ( Salmo salar ) in a controlled challenge test. We used a statistical model that distinguishes between survival time for the fish that died and the ability to survive the entire test as two separate traits. Eleven of the 49 full-sib families suffered 100% mortality, 15 families had between 10% and 25% survival, and the four least affected families had survival rates between 36% and 48%. Estimated heritability of survival on the liability scale was 0.32 ± 0.10. Time until death for fish that died during the test and the ability to survive the entire test were not expressions of the same genetic trait. Simply selecting survivors as parents for the next generation is expected to more than double the overall survival rate in only one generation, given similar exposure to the parasite. Improving the genetic capacity to survive the infection will probably not eradicate the parasite, but when used as a disease control measure, such improvement may contain the infection at a level where the parasite ceases to be a major problem.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.074
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.265
Teacher spread0.252 · 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 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

Citations31
Published2009
Admission routes1
Has abstractyes

Explore more

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