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Record W4321473969 · doi:10.1139/cjfas-2022-0259

A century-long time series reveals large declines and greater synchrony in Nass River sockeye salmon size-at-age

2023· article· en· W4321473969 on OpenAlexaffvenue
Cameron Freshwater, Will Duguid, Francis Juanes, Skip McKinnell

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of VictoriaFisheries and Oceans Canada
Fundersnot available
KeywordsFecundityOncorhynchusOverexploitationBiologyDemographyProductivityEcologyBroodLife historyLife history theoryFisheryPopulationFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The ability to comprehend the nature of changes in body size is often limited by time series of relatively short duration. Using archival records of 118 573 individual measurements, we developed a 106-year time series of mean size-at-age, by sex, of Nass River sockeye salmon ( Oncorhynchus nerka). Size-at-age declined during the last century in several distinct stanzas. Until the 1930s, there was weak covariation in size-at-age among age classes of both sexes. Thereafter, all time series exhibited a coherent cyclical pattern, superimposed on an underlying decline, reaching smallest average size-at-age in 2019. Age classes sharing the same years of ocean growth had more similar patterns of variation than those sharing a common brood year, suggesting a dominant role of marine life history. Since 1914, mean size-at-age declined from 5% up to 13% depending on age class and sex, resulting in an estimated 7%–19% decline in fecundity, which is likely to reduce the productivity of these populations. In the absence of increased survival, management targets based on fixed adult escapements may result in overexploitation.

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)0.001
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 score0.990
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.233
Teacher spread0.217 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

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