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Thermal habitat use and juvenile growth of Svalbard Arctic charr: evidence from otolith stable oxygen isotope analyses

2011· article· en· W1776535407 on OpenAlexaff
Jane Aanestad Godiksen, Michael Power, Reidar Borgstrøm, J. Brian Dempson, Martin‐A. Svenning

Bibliographic record

VenueEcology Of Freshwater Fish · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans CanadaUniversity of Waterloo
Fundersnot available
KeywordsOtolithSalvelinusArcticJuvenileHabitatEnvironmental scienceLittoral zoneEcologyAir temperatureOceanographyFisheryAtmospheric sciencesBiologyGeologyTroutFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract – Stable oxygen isotopes (δ 18 O) derived from otoliths were used to estimate mean annual water temperatures experienced by individual Svalbard Arctic charr, Salvelinus alpinus (L.), during their first four growth seasons. The analysed Arctic charr experienced a high variety of temperatures, indicating the use of different thermal habitats. A higher proportion of the juveniles experienced warmer temperatures during their first summer compared with later summers, suggesting the selective use of the shallowest littoral areas of the lake. Although the estimated temperatures were consistent with water temperatures found in High Arctic rivers and lakes during summer, they did not represent the annual variation in air temperature registered over the 20 years of otolith measurement. Furthermore, summer otolith increment width did not correlate with the experienced temperature. However, after the second year, otolith increment width was highly dependent on increment width during the previous summer. This study estimated mean summer water temperatures experienced by individual Arctic charr during the first four growth seasons providing additional evidence that stable oxygen isotope analysis can be used to provide insight into the thermal habitat use by juvenile Arctic charr.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.994

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.051
GPT teacher head0.240
Teacher spread0.189 · 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.

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

Citations17
Published2011
Admission routes1
Has abstractyes

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