MétaCan
Menu
Back to cohort
Record W2055024990 · doi:10.1139/f03-154

Using an individual-based model of fish assemblages to study the response of size spectra to changes in fishing

2004· article· en· W2055024990 on OpenAlexvenueno aff
Yunne‐Jai Shin, Philippe Cury

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsPredationFishingEcologyEcosystemBiologyForage fishAbundance (ecology)Fish <Actinopterygii>FisheryEnvironmental scienceStatisticsMathematics

Abstract

fetched live from OpenAlex

For most fish species, strong environmental constraints imposed by living in an aquatic medium have produced converging streamlined body forms without prehensile appendices. This similarity in body shapes highlights a common predation constraint: a predatory fish must have a jaw large enough to swallow its prey. Fish diets may then reflect local prey availability and predator–prey size ratios. Based on this size-based opportunistic predation process, the multispecies individual-based model OSMOSE (Object-oriented Simulator of Marine ecOSystem Exploitation) is used to investigate to what extent the size distribution of fish communities can contribute to better our understanding of the functioning of marine food webs and the ecosystem effects of fishing. Strong similarity in shape is found between simulated size spectra and those described in empirical studies. The existence of a curvature towards small size classes is discussed in the light of the size-based predation hypothesis, which implies that smaller fish may undergo higher predation mortality. Applying linear and quadratic regressions to the simulated size spectra allows the detection of variations in fishing pressure and the proposal of different ways to quantify them. In particular, it is shown that the slope of the size spectrum decreases quasilinearly with fishing mortality and that the curvature could help to detect ecosystem overexploitation.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.347
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 science0.0010.000
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 head0.088
GPT teacher head0.301
Teacher spread0.212 · 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

Citations257
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicMarine and fisheries researchFrench-language works237,207