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Determining the consistency of thermal habitat segregation within and among Arctic charr morphotypes in Gander Lake, Newfoundland

2011· article· en· W1896392216 on OpenAlexafffundabout
Michael Power, Michael O’Connell, Brian Dempson

Bibliographic record

VenueEcology Of Freshwater Fish · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans CanadaUniversity of Waterloo
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsProfundal zoneHabitatSalvelinusEcologyForagingArcticCompetition (biology)PredationGeographyBiologyFisheryFish <Actinopterygii>Littoral zoneTrout

Abstract

fetched live from OpenAlex

Abstract – Otolith carbon and oxygen isotope data obtained from distinct genetic and ecological groups of lacustrine Arctic charr, Salvelinus alpinus L., from Gander Lake, Newfoundland, were used to examine hypotheses regarding the consistency of differential habitat use among the groups. Results indicated thermal habitat separation by group, with small ‘pale’ individuals consistently remaining in cooler profundal habitats and larger ‘dark’ individuals more frequently occupying warmer upper water column habitats. Theoretical measures of resource separation and competition indicated lower thermal habitat overlap among the forms and greater within‐form competition. Depth at capture data indicated more varied short‐term use of available lake habitats by ‘dark’ form Arctic charr, possibly as a result of cannibalistic foraging on profundal ‘pale’ form fish. Nominal capture depth data only partially explained observed variation in the mean temperature of occupied thermal habitat, suggesting that capture depth can only be used as a rough index of thermal habitat use. Provided that sufficient thermal gradients exist in the environments being studied, otolith oxygen isotopes provide a useful means of establishing the significance of niche differentiation among individuals.

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 score0.490
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.015
GPT teacher head0.191
Teacher spread0.176 · 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

Citations25
Published2011
Admission routes3
Has abstractyes

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