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Record W2124881174 · doi:10.1139/f07-038

Is growth a reliable indicator of habitat quality and essential fish habitat for a juvenile estuarine fish?

2007· article· en· W2124881174 on OpenAlexvenueno aff
Steven P. Searcy, David B. Eggleston, Jonathan A. Hare

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsOtolithHabitatJuvenileAbiotic componentEstuaryJuvenile fishEcologyFisheryBiologyNursery habitatAbundance (ecology)Environmental scienceFish <Actinopterygii>

Abstract

fetched live from OpenAlex

A common assumption throughout the marine ecological and fisheries literature is that growth is a valid indicator of habitat quality and can be used as a criterion for designation of essential fish habitat (EFH). In this study, the validity of growth as an index of habitat quality was tested by examining how variability in otolith growth was related to abiotic and biotic environmental conditions and could be biased by previous growth history, density dependence, and selective mortality. The study was conducted with juvenile Atlantic croaker (Micropogonias undulatus) collected in two North Carolina, USA, estuaries during two seasons of two recruitment years. Water temperature, a component of habitat quality, explained nearly 40% of the variability in juvenile otolith growth. There was also evidence that estimates of growth could be biased by density dependence (slower growth at higher conspecific abundance) and by selective mortality (higher mortality of individuals with relatively slower larval and juvenile otolith growth). Studies using growth-based assessment of habitat quality that fail to identify factors underlying growth rate differences among habitats may reach incorrect decisions regarding quality of different habitats and assignment of EFH.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.026
GPT teacher head0.275
Teacher spread0.249 · 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

Citations55
Published2007
Admission routes1
Has abstractyes

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