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Record W4394610681 · doi:10.3390/fishes9040130

The Relationship between Mean Length at Maturity and Maximum Length in Coral Reef Fish

2024· article· en· W4394610681 on OpenAlexaff
Elaine Chu, Daniel Pauly

Bibliographic record

VenueFishes · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFish Biology and Ecology Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCoralFisheryReefMaturity (psychological)Coral reefFish <Actinopterygii>Coral reef fishBiologyMathematicsAnimal scienceEcologyPsychology

Abstract

fetched live from OpenAlex

This article proposes a mechanism that triggers first maturation and spawning in coral reef (bony) fish, which allows for predicting their length at first maturity. Thus, mean lengths at first maturity (Lm) and the corresponding maximum lengths (Lmax) in 207 populations of 131 species of coral reef fish were assembled and used to test the hypotheses that (a) there is, in coral reef fish, a single value of a size-related parameter acting as a trigger for their maturation and eventual spawning, and (b) that this single value is statistically the same as that published previously for other bony fish. The results, based on the assembled Lm and Lmax data and on estimates of the parameter D, which link the length of fish with the relative surface of their gills, covered 44 families and Lmax values ranging from 1.8 to 181.6 cm and confirmed that the threshold in (a) exists. Also, we assessed (in b) that this threshold value, i.e., LmaxD/LmD = 1.35 (±0.02), is not statistically different from similar estimates for other groups of teleosts, notably semelparous salmonids, cichlids, sturgeons and Chinese and Turkish freshwater and marine fish. One implication is that given ocean warming and deoxygenation, coral reef fish will not only be smaller than they currently are, but also mature and spawn at smaller sizes, and thus produce fewer, smaller eggs.

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 score0.036
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.048
GPT teacher head0.245
Teacher spread0.197 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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