MétaCan
Menu
Back to cohort

The influence of temperature on advective loss of Atlantic cod (<i>Gadus morhua</i>) eggs from the inshore environment

2001· article· en· W2038705552 on OpenAlexaffabout
Ian Bradbury, Paul V. R. Snelgrove, Sandra Fraser

Bibliographic record

VenueFisheries Oceanography · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans CanadaMemorial University of Newfoundland
Fundersnot available
KeywordsGadusIchthyoplanktonAdvectionBayAtlantic codOceanographyFisheryPredationEstuaryEnvironmental scienceBiologyFish <Actinopterygii>EcologyGeology

Abstract

fetched live from OpenAlex

We use a simple model of temperature‐dependent egg development and mortality to develop several hypotheses concerning the effect of temperature on the occurrence of eggs of Atlantic cod, Gadus morhua, in Placentia Bay, Newfoundland. Predictions from this exploratory model were tested through a series of synoptic ichthyoplankton surveys throughout spawning and postspawning periods during 1997 and 1998. Although several egg mortality relationships were explored through the simulation, a constant mortality rate best represented the pattern observed in the two years of data. Peaks in late stage egg densities occurred in August of both 1997 and 1998 and were apparently decoupled from egg production peaks in April. We observed a decrease in mortality and the distance dispersed during egg development with increases in water temperature. We suggest that the effects of predation are small relative to the advective effects within this system, and that the interaction between advection and temperature‐dependent vital rates of eggs may have dramatic consequences for coastal retention of propagules produced by inshore spawning events.

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)0.001
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 score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations18
Published2001
Admission routes2
Has abstractyes

Explore more

Same venueFisheries OceanographySame topicMarine and fisheries researchFrench-language works237,207