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Record W2143599510 · doi:10.1089/zeb.2008.0551

Pigments, Patterns, and Fish Behavior

2008· review· en· W2143599510 on OpenAlexaff
Anna C. Price, Cameron J. Weadick, Janet K. Shim, F. Helen Rodd

Bibliographic record

VenueZebrafish · 2008
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologyMatingEvolutionary biologyColor visionFish <Actinopterygii>EcologyBehavioral ecologyPerceptionFish physiologyAggressionBehavioral patternZoologyNeuroscienceArtificial intelligencePsychologyFishery

Abstract

fetched live from OpenAlex

Color patterns in fish are often multicomponent signals, composed of pigment-based and structural color patches that can be used to communicate within species, in both inter- and intrasexual interactions, and between species. In this review, we discuss some of the roles played by pigment-based elements of color pattern. We begin by discussing general forms of coloration, classifying them by appearance (e.g., cryptic vs. conspicuous) and apparent function (e.g., conspicuous coloration and mating displays, stripes and cooperation, and bars and aggression). We then briefly discuss the roles pigments play in the perception of these color patterns via their presence in the eye. In the last section, we look at the relative importance of carotenoid versus melanic coloration in situations where honest signals to potential rivals and potential mates might be required. In this survey, we have highlighted some recent research, especially studies that consider both the physiological and behavioral processes underlying the evolution and expression of pigment-based color patterns in fish. The nature of pigmented color patterns depends not just on the dynamics of pattern development and physiological regulation, but also on the behavioral roles played by these patterns, both now and in the past. As such, advances in particular fields of study on pigment patterns (physiology, developmental biology, behavioral ecology, evolutionary biology, etc.) will increasingly depend on insights from other fields.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.056
GPT teacher head0.279
Teacher spread0.222 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations210
Published2008
Admission routes1
Has abstractyes

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