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Record W3035973589 · doi:10.1111/eff.12556

Migratory salmon smolts exhibit consistent interannual depensatory predator swamping: Effects on telemetry‐based survival estimates

2020· article· en· W3035973589 on OpenAlexafffundabout
Nathan B. Furey, Eduardo G. Martins, Scott G. Hinch

Bibliographic record

VenueEcology Of Freshwater Fish · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British ColumbiaUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsOncorhynchusTelemetryPredationPredatorJuvenilePopulationFisheryHatcheryBiologySwampEcologyGeographyFish <Actinopterygii>Demography

Abstract

fetched live from OpenAlex

Abstract Migrations of juvenile salmon smolts are generally high‐risk, with predation often implicated in reduced survival. In theory, smolts can maximise survival via depensation, or synchronising movements to swamp predators. Depensation, however, is difficult to assess in the wild. Accounting for depensation could also generate more realistic telemetry‐based survival estimates for management. Here, we assess six years (2010–2014, 2016) of acoustic telemetry and outmigration density data for sockeye salmon ( Oncorhynchus nerka ) from Chilko Lake, British Columbia, Canada. Prevoiusly, depensation for this population wasassessed for a single year, but interannual consistency is not known. We found evidence of depensation in each year, although its strength varied. In addition, by integrating depensation with outmigration densities, annual population‐level survival estimates in this initial (14‐km) migratory segment increased by 0.02–0.24 relative to previously published estimates. However, when extending these survival rates from the first 14 km through the entire tracked migration (1,044 km), increases in estimates were small (~0.01). Potential conservation and management applications of depensation include implications for recovering imperiled populations and informing hatchery release strategies.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.215
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 teacher head, not a consensus.

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

Citations25
Published2020
Admission routes3
Has abstractyes

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