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Record W3012099936 · doi:10.1111/fog.12471

Running the trophic gauntlet: Empirical support for reduced foraging success in juvenile salmon in tidally mixed coastal waters

2020· article· en· W3012099936 on OpenAlexafffund
Samantha James, Evgeny A. Pakhomov, Natalie Mahara, Brian P. V. Hunt

Bibliographic record

VenueFisheries Oceanography · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsTula FoundationUniversity of British ColumbiaFisheries and Oceans Canada
FundersHakai Institute
KeywordsForagingTrophic levelPredationJuvenileFisheryZooplanktonProductivityEcologyBiologyTrophic cascadeOptimal foraging theoryGeographyFood web

Abstract

fetched live from OpenAlex

Abstract The productivity of Fraser River sockeye salmon has declined in recent years, with 2019 being the lowest return on record. The cause of the decline is still not fully understood; however, bottom‐up drivers and trophic interactions during the early marine migration are considered to be important contributing factors. McKinnell et al. (Fisheries Oceanography, 23, 2014 and 322) developed a “trophic gauntlet hypothesis,” proposing that low biological productivity leads to an energy deficit from poor foraging opportunities in migrating salmon. When combined with poor foraging conditions in typically productive waters elsewhere on the migration, low marine survival may result. Our study examined prey availability and stomach fullness of juvenile sockeye salmon along the 120 km stretch of the coastal migration through the Discovery Islands and Johnstone Strait to determine whether this section of the migration is indeed food limited. We observed low stomach fullness throughout tidally mixed waters, providing empirical support for the trophic gauntlet hypothesis. Zooplankton abundance was high in these regions so it appeared that unfavourably small prey size may have been the cause of low foraging success. We also observed foraging hotspots at both ends of the gauntlet, indicating that such areas may be key feeding grounds for migratory salmon.

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 score0.025
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.237
Teacher spread0.216 · 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

Citations18
Published2020
Admission routes2
Has abstractyes

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