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Record W2000674673 · doi:10.1139/f04-223

Is metabolic rate a reliable predictor of growth and survival of brown trout (<i>Salmo trutta</i>) in the wild?

2005· article· en· W2000674673 on OpenAlexvenueno aff
David Álvarez, Alfredo G. Nicieza

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSalmoBrown troutJuvenileBiologyMetabolic rateGrowth rateTroutZoologySalmonidaeEcologyBasal metabolic rateSmoltificationFish <Actinopterygii>FisheryEndocrinology

Abstract

fetched live from OpenAlex

In salmonids, there seems to be a positive correlation between standard metabolic rate and growth rate under artificial rearing conditions. Several recent studies have suggested that phenotypic correlations between physiological or behavioural traits and developmental or life history responses might be common when assayed in low-complexity habitats but rare in those with a high degree of spatiotemporal complexity. This study provides the first test of the connection between metabolic and growth rates of juvenile brown trout (Salmo trutta) in natural streams. In two out of four streams, there was no relationship between metabolic rates and subsequent growth, whereas in the two others, growth and metabolic rates were negatively correlated. Furthermore, survival rates were either unaffected or negatively correlated with metabolic rates. These results reveal complex relationships between metabolic rate, growth, and environmental variability and suggest that (i) in the wild, negative selection on high metabolic rates may result from both juvenile mortality and reduced growth rates, (ii) the conclusions derived from laboratory experiments are not directly applicable to natural populations, and (iii) the correlations between metabolic rate and growth can prove useful after selection of the appropriate spatial and temporal scales.

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)0.001
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 score0.065
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.013
GPT teacher head0.202
Teacher spread0.190 · 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

Citations127
Published2005
Admission routes1
Has abstractyes

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