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Behavioural ecology at sea of Atlantic salmon (Salmo salar L.) kelts from a Newfoundland (Canada) river

2011· article· en· W2134739734 on OpenAlexafffundabout
David G. Reddin, Peter Downton, Ian Fleming, L. P. Hansen, Andrew R. Mahon

Bibliographic record

VenueFisheries Oceanography · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMemorial University of NewfoundlandFisheries and Oceans Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSalmoFisheryRange (aeronautics)OceanographyHabitatEnvironmental scienceFish <Actinopterygii>SalmonidaeEcologyBiologyGeology

Abstract

fetched live from OpenAlex

For migratory fishes such as Atlantic salmon, temporal variation in the utilization of thermal and depth habitats in the marine environment is likely to occur at both broad and fine scales, reflecting both ecological and metabolic factors. To test this, we implanted 26 Atlantic salmon kelts (i.e., post-spawners) descending Campbellton River, Newfoundland, with data storage tags that recorded date/time, internal and external temperatures, pressure, and light. The salmon that returned to Campbellton River after an average of 62 days at sea were consecutive spawning kelt. Detailed data were recovered from eight of these fish and revealed distinct patterns in their utilization of thermal and depth habitats. Water temperatures experienced over the period at sea showed a wide range for all fish (from <0 to nearly 20°C); however, there were two clear frequency modes; one at 6–7°C and the other at 11–12°C. The cooler mode was indicative of daytime profiles and the warmer mode of night-time profiles. This corresponded with the depth profiles, which indicated that salmon dove more frequently below 5 m (mean ± SD = 23 ± 0.9 dives per day) and spent less time (approximately 18%) near the sea surface (<1 m) during the day than during the night (4 ± 0.4 dives per night; approximately 45% time <1 m). The diurnal pattern may be indicative of a reliance on vision for feeding at depth and the short duration of dives (<10 min) may be a strategy that allows salmon a metabolic advantage (e.g., swimming capacity) over their prey living constantly at depth in cool waters.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.993

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.179
Teacher spread0.165 · 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.

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

Citations41
Published2011
Admission routes3
Has abstractyes

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