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Record W2973314482 · doi:10.1242/jeb.208249

Brain cooling marginally increases acute upper thermal tolerance in Atlantic cod

2019· article· en· W2973314482 on OpenAlexafffund
Fredrik Jutfelt, Dominique G. Roche, Thomas D. Clark, Tommy Norin, Sandra A. Binning, Ben Speers‐Roesch, Mirjam Amcoff, Rachael Morgan, Anna H. Andreassen, Josefin Sundin

Bibliographic record

VenueJournal of Experimental Biology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of New BrunswickUniversité de MontréalCarleton University
FundersAustralian Research CouncilNatural Sciences and Engineering Research Council of CanadaNorges ForskningsrådVetenskapsrådetDanmarks Frie ForskningsfondCarl Tryggers Stiftelse för Vetenskaplig ForskningSvenska Forskningsrådet FormasRoyal Swedish Academy of Sciences
KeywordsAtlantic codEnvironmental scienceOceanographyGeologyFisheryBiology

Abstract

fetched live from OpenAlex

Physiological mechanisms determining thermal limits in fishes are debated but remain elusive. It has been hypothesised that motor function loss, observed as loss of equilibrium during acute warming, is due to direct thermal effects on brain neuronal function. To test this, we mounted cooling plates on the heads of Atlantic cod (Gadus morhua) and quantified whether local brain cooling increased whole-organism acute upper thermal tolerance. Brain cooling reduced brain temperature by 2-6°C below ambient water and increased thermal tolerance by 0.5 and 0.6°C on average relative to instrumented and uninstrumented controls, respectively, suggesting that direct thermal effects on brain neurons may contribute to setting upper thermal limits in fish. However, the improvement in thermal tolerance with brain cooling was small relative to the difference in brain temperature, demonstrating that other mechanisms (e.g., failure of spinal and peripheral neurons, or muscle) may also contribute to controlling acute thermal tolerance.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.999

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)0.0020.000

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 head0.010
GPT teacher head0.256
Teacher spread0.246 · 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 designBench or experimental
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

Citations61
Published2019
Admission routes2
Has abstractyes

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