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Record W3033852209 · doi:10.1139/cjfas-2019-0395

Migration of Atlantic salmon (<i>Salmo salar</i>) smolts in a large hydropower reservoir

2020· article· en· W3033852209 on OpenAlexafffundvenue
Amanda B. Babin, Mouhamed Ndong, Katy Haralampides, Stephan J. Peake, Ross A. Jones, R. Allen Curry, Tommi Linnansaari

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans CanadaUniversity of New Brunswick
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsSalmoEstuaryHydropowerFisheryFish migrationEnvironmental scienceSpring (device)Fish <Actinopterygii>SalmonidaeHydrology (agriculture)BiologyEcologyGeology

Abstract

fetched live from OpenAlex

Migration rates, delay, timing, and success of acoustic-tagged Atlantic salmon (Salmo salar) presmolts (n = 120) and smolts (n = 57) are reported as they moved through the large Mactaquac Generating Station (MGS) reservoir and subsequently the lower Saint John River (SJR). The potential relationship between fish movements and the MGS operations was examined directly and via a hydrodynamic model. Migration rates were 15.4–29.3 km·day−1 within the river sections and 5.0–13.3 km·day−1 through the reservoir, a significant reduction of 32%–57%. Migratory timing was temporally mismatched with dam operations such that only a few (n = 3) smolts had the option of dam passage via spill. Migration success estimated as apparent survival was high through the reservoir (81%–100%), declined by 8%–32% during passage at the MGS, and additional losses (27%–55%) occurred during the migration to the lower SJR, such that overall survival to the estuary for the groups tagged as autumn presmolts was 61%–65%, and survival for those tagged as spring smolts was 6%–10%.

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 score0.036
Threshold uncertainty score0.071

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 head0.015
GPT teacher head0.213
Teacher spread0.198 · 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

Citations11
Published2020
Admission routes3
Has abstractyes

Explore more

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