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The energetic equivalence of changing salinity and temperature to juvenile salmon

2006· article· en· W2013782519 on OpenAlexafffund
Sandra J. Webster, L. M. Dill

Bibliographic record

VenueFunctional Ecology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsSimon Fraser University
FundersFisheries and Oceans CanadaWatershed Watch Salmon SocietyPADI Foundation
KeywordsSalinityBiologyJuvenileSaline waterHabitatOsmoregulationJuvenile fishTemperature salinity diagramsForageEcologyWater columnAnimal scienceFishery

Abstract

fetched live from OpenAlex

Summary The influence of salinity, temperature and physiological development on habitat choice by juvenile salmon is poorly understood. We measured differences in the net energetic cost of habitats that differed in salinity or temperature using behavioural titration of juvenile salmon and correlated these costs with body size and osmoregulatory enzyme activity to quantify how costs change with physiological development. Juvenile salmon showed a strong preference for saline water (27 or 15 vs 0) and for cold water (9 °C vs 14 °C). It was estimated to be 0·15 W and 0·11 W more costly for salmon to forage in fresh water than in 15 and 27 salt water, respectively, and 0·09 W more costly to forage in warm than in cold water. We expected fish to prefer 15 salt water to fresh water regardless of enzyme activity because they are iso‐osmotic with this salt concentration. In contrast, preference for higher salt concentrations should increase with enzyme activity. Consistent with our expectations, enzyme activity was not correlated with preference for 15 salt water, but was positively correlated with preference for 27 salt water. The cost of changing salinity vs changing temperature were very similar, indicating that knowledge of both of these costs are necessary to understand juvenile salmon habitat choice.

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 score0.227
Threshold uncertainty score0.655

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 head0.006
GPT teacher head0.192
Teacher spread0.187 · 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

Citations30
Published2006
Admission routes2
Has abstractyes

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