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Lack of glucose and hsp70 responses in haddock <i>Melanogrammus aeglefinus </i>(L.) subjected to handling and heat shock

2008· article· en· W2119059759 on OpenAlexaff
Luis O.B. Afonso, S. Hosoya, Jason W. Osborne, A. Kurt Gamperl, Stewart C. Johnson

Bibliographic record

VenueJournal of Fish Biology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsNational Research Council CanadaInstitute for Marine BiosciencesMemorial University of Newfoundland
Fundersnot available
KeywordsHaddockBiologyGadusGadidaeHsp70JuvenileShock (circulatory)EndocrinologyInternal medicineAtlantic codStressorFisheryHeat stressHeat shock proteinZoologyFish <Actinopterygii>Animal scienceEcologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

Juvenile haddock Melanogrammus aeglefinus (c. 39 g) were exposed to either a handling stressor (1 min out of water) or heat shock (increase from 10 to 15° C for 1 h), and plasma cortisol, plasma glucose and gill hsp70 levels were determined before, and at 1, 3, 6, 12, 24 and 48 h post‐stress. The pattern of cortisol increase was similar following both stressors, with levels increasing by 25‐fold at 1 h post‐stress, but returning to pre‐stress levels (2–5 ng ml−1) by 3 h. In contrast, neither handling nor heat shock caused an increase in plasma glucose levels. Although gill hsp70 was detected, presumably constitutive levels, in both control and heat shocked groups, there were not significant changes in gill hsp70 levels after exposure to heat shock. The lack of glucose and hsp70 responses to these typical stressors is consistent with previous studies on Atlantic cod Gadus morhua, and suggests that the stress physiology of Gadidae differs from the ‘typical’ teleost.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.039
GPT teacher head0.268
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations32
Published2008
Admission routes1
Has abstractyes

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