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Record W4387976049 · doi:10.1002/aff2.134

Biotic influences on drift behaviour of larval white sturgeon (<i>Acipenser transmontanus</i>)

2023· article· en· W4387976049 on OpenAlexafffund
Angie Coulter, D. Steven O. McAdam, John S. Richardson

Bibliographic record

VenueAquaculture Fish and Fisheries · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsGovernment of British ColumbiaUniversity of British Columbia
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsSturgeonLarvaBiologyAcipenserEcologyHabitatMesocosmLake sturgeonZoologyFisheryFish <Actinopterygii>Nutrient

Abstract

fetched live from OpenAlex

Abstract Drift by larval white sturgeon ( Acipenser transmontanus ) results in an ontological habitat shift during early life that may be influenced by changes in the trade‐off between mortality risk and growth potential. Despite the importance of early life history to recruitment and conservation, for many species, including white sturgeon, we have a limited understanding of the mechanistic drivers of drift. We tested if two biotic factors, conspecific density and timing of food availability during the yolksac larvae stage, influenced the timing of drift behaviour. We evaluated larval drift timing for yolksac larvae reared in laboratory mesocosms at two densities (10 or 20 larvae) and three feeding initiation times (before exogenous feeding, at the initiation of exogenous feeding, or starvation). We found that drift occurred at 13 days post‐hatch (dph) overall, 2 days after the shift from the yolksac stage to the feeding stage (11 dph at 14°C). The timing of food availability in the fed treatments did not affect the timing of larval drift, nor did the density of conspecifics. Starvation delayed drift timing by 2 days, to15 dph. This delay of drift from a habitat with no food availability may disadvantage starving larvae and reduce growth potential.

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 score0.189
Threshold uncertainty score0.682

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 studies0.0000.000
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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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