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Record W2084534916 · doi:10.1577/t04-119.1

Migration Timing and River Survival of Late‐Run Fraser River Sockeye Salmon Estimated Using Radiotelemetry Techniques

2005· article· en· W2084534916 on OpenAlexaff
Karl K. English, William R. Koski, Cezary Sliwinski, Anita Blakley, Alan J. Cass, James C. Woodey

Bibliographic record

VenueTransactions of the American Fisheries Society · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
FundersU.S. Department of StateU.S. Department of Commerce
KeywordsOncorhynchusFisheryFish <Actinopterygii>Environmental scienceGeographyBiology

Abstract

fetched live from OpenAlex

Abstract In recent years, large numbers of late‐run Fraser River sockeye salmon Oncorhynchus nerka have died in freshwater areas before they spawned. We used radiotelemetry techniques to determine river entry timing, migration rates, and survival for the summer and late runs of sockeye salmon in 2002 and 2003. Fishery removals and river detections accounted for 62% of the 873 fish tagged and released in marine areas in 2002. Some late‐run fish migrated from release sites to the Mission hydroacoustic site in 8–10 d and migrated upstream with the summer run. Most late‐run fish remained in the Strait of Georgia for 15–33 d and entered the river after the summer run. Tracking data indicated that individual sockeye salmon maintained essentially the same chronological order as they migrated up the Fraser and Thompson rivers. Summer‐run stocks traveled faster between Mission and the Thompson Junction (33–39 km/d) than late‐run fish (17–21 km/d). After accounting for fishery removals, the river survival rate for summer‐run fish was 92%, and differences between the three release timing groups were not significant. In contrast, the first migration group of late‐run fish that passed Mission had significantly lower survival (13%) than all other timing groups. The survival rate for the second and third river entry groups combined (74%) was significantly lower than that of the fourth group (92%). Period‐specific survival rates were used to define a relationship between river entry timing and survival and to identify additional factors influencing river entry timing for late‐run fish. These relationships may be useful in predicting river survival rates for late‐run stocks in future years.

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 score0.032
Threshold uncertainty score0.792

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 studies0.0000.002
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 head0.018
GPT teacher head0.244
Teacher spread0.227 · 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

Citations75
Published2005
Admission routes1
Has abstractyes

Explore more

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