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Record W2031817376 · doi:10.1139/z02-199

Monopolization of food by zebrafish (<i>Danio rerio</i>) increases in risky habitats

2002· article· en· W2031817376 on OpenAlexfundvenueno aff
Ian M. Hamilton, Lawrence M. Dill

Bibliographic record

VenueCanadian Journal of Zoology · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser University
KeywordsHabitatMonopolizationForagingBiologyEcologyPredationDanioZebrafishFishery

Abstract

fetched live from OpenAlex

Dominant zebrafish (Danio rerio) previously have been shown to reduce their monopolization of food when foraging in structurally complex habitats compared with open habitats. Complex habitats may be more difficult to defend but may also be safer. To decouple these effects, we compared aggression and monopolization of food in groups of zebrafish foraging in an open habitat and one with overhead cover, as well as in an open habitat and a complex (vegetated) habitat. Covered and open habitats should have been equally defendable. In our experiments, fish used covered habitats more than open ones, suggesting that the perceived risk of predation was lower in covered habitats. There was no difference in use of vegetated and open habitats, suggesting that these habitats, which should differ in defendability, did not differ in safety. We found that the degree of food monopolization (expressed in the coefficient of variation within groups) at risky feeders was significantly greater in open habitats than in covered, but not vegetated, habitats. We did not find a difference in aggression between habitats. These results indicate that resource monopolization in groups of zebrafish is greater in risky habitats and support the hypothesis that the lower monopolization of food in complex habitats could result from greater safety in those habitats rather than, or in addition to, the reduction in defendability.

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 score0.133
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 studies0.0000.000
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 head0.019
GPT teacher head0.197
Teacher spread0.178 · 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

Citations56
Published2002
Admission routes2
Has abstractyes

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