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Record W4401700855 · doi:10.1139/cjfas-2024-0012

Variation in estuary use patterns of juvenile Chinook salmon in the Fraser River, BC

2024· article· en· W4401700855 on OpenAlexafffundvenue
David C. Scott, Lia Chalifour, Misty MacDuffee, Julia K. Baum, Terry D. Beacham, Éric Rondeau, Scott G. Hinch

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of VictoriaFisheries and Oceans CanadaRaincoast Conservation FoundationUniversity of British Columbia
FundersFisheries and Oceans CanadaRaincoast Conservation FoundationNatural Sciences and Engineering Research Council of CanadaPacific Salmon FoundationMarine Environmental Observation Prediction and Response Network
KeywordsChinook windEstuaryJuvenileFisheryOncorhynchusVariation (astronomy)BiologyGeographyEcologyFish <Actinopterygii>Environmental science

Abstract

fetched live from OpenAlex

Juvenile Pacific salmon ( Oncorhynchus spp.) use estuary habitats to varying degrees with some species and populations thought to rely heavily on these areas for early growth. In the Fraser River, British Columbia, there are 18 distinct conservation units of Chinook salmon ( O. tshawytscha), and all but one is of conservation concern. Our study compares the outmigration timing, size, and habitat use of juvenile Chinook salmon in the Fraser River estuary. Over 5 years (2016–2020), we captured 6493 juvenile Chinook salmon, with 3318 sampled for stock identification. Fraser River Chinook salmon extensively used estuary habitats, but patterns varied considerably by population. Juvenile Chinook salmon from the Lower Fraser River were most abundant and present the longest, arriving the smallest in late March and early April, and captured until July. South Thompson ocean-type Chinook salmon entered the estuary later, starting to arrive in late May or early June and remaining present until mid-August. Overall, juvenile Chinook salmon varied considerably in their estuary use across populations. Understanding this variation can inform differences in productivity and guide recovery actions.

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 score0.265
Threshold uncertainty score0.533

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.014
GPT teacher head0.208
Teacher spread0.193 · 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

Citations3
Published2024
Admission routes3
Has abstractyes

Explore more

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