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Record W1796212355 · doi:10.1139/cjfas-2014-0560

Marine migration and habitat use of anadromous brown trout (<i>Salmo trutta</i>)

2015· article· en· W1796212355 on OpenAlexaffvenue
Sindre Håvarstein Eldøy, Jan Grimsrud Davidsen, Eva B. Thorstad, Frederick G. Whoriskey, Kim Aarestrup, Tor F. Næsje, Lars Rønning, Aslak Darre Sjursen, Audun H. Rikardsen, Jo Vegar Arnekleiv

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsOcean Tracking NetworkDalhousie University
Fundersnot available
KeywordsSalmoFish migrationBrown troutFisheryHabitatTroutSalmonidaeBiologyEcologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The biology and ecology of anadromous brown trout (Salmo trutta) at sea is poorly understood. This study provided information on spatial and temporal distribution of sea trout in the ocean. The behaviour of 115 individuals (veteran migrants, 270–700 mm) was tracked by using acoustic telemetry in a fjord system during April–September in 2012–2013. Overall, fish spent 68% of their marine residence time close to river mouths (<4 km). Most fish registrations (75%) were in nearshore habitats, but pelagic areas were also used. The maximum migration distance of tagged fish was categorized as short (<4 km from river mouth, 40% of fish), medium (4 – ∼13 km, 18% of fish), or long (>∼13 km, 42% of fish). Long-distance migrants had poorer body condition in spring prior to migration, used pelagic areas more often, and returned earlier to fresh water than short- and medium-distance migrants. Marine residence time was 7–183 days and was positively correlated to body length and smolt age, but negatively correlated to the date of sea entry.

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)0.000
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 score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.027
GPT teacher head0.205
Teacher spread0.179 · 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

Citations73
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicFish Ecology and Management Studies→French-language works237,207→