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Record W1987723096 · doi:10.1139/f02-117

The effects of environmental heat stress on heat-shock mRNA and protein expression in Miramichi Atlantic salmon (<i>Salmo salar</i>) parr

2002· article· en· W1987723096 on OpenAlexvenueno aff
Susan G. Lund, Daniel Caissie, Richard A. Cunjak, Mathilakath M. Vijayan, Bruce L. Tufts

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsnot available
Fundersnot available
KeywordsSalmoHeat shock proteinMessenger RNAHsp70Heat stressBiologyHeat shockZoologySalmonidaeShock (circulatory)Gene expressionFish <Actinopterygii>Animal scienceAndrologyFisheryEcologyInternal medicineGeneBiochemistryMedicine

Abstract

fetched live from OpenAlex

This study combines laboratory experiments with temperature monitoring and fish sampling in the wild to determine if Atlantic salmon (Salmo salar) parr from the Miramichi River in New Brunswick are currently experiencing significant sublethal heat stress during the warm summer months. Laboratory experiments indicated that Hsp 70 mRNA and protein and Hsp 30 mRNA were all significantly induced in Atlantic salmon parr between 22°C and 25°C. Field sampling during moderate spring temperatures and a high-temperature event in summer further indicated that the threshold for mRNA induction of both Hsp 70 and Hsp 30 is around 23°C, but Hsp 70 protein levels were only significantly elevated in the field at 27°C. Hsc 70 mRNA and protein levels were not significantly increased during heat stress under laboratory conditions. In the field, however, Hsc 70 mRNA was significantly increased at 23°C and both Hsc 70 mRNA and protein levels were elevated at 27°C. Taken together, the results of this investigation suggest that Atlantic salmon parr from the Miramichi River are currently experiencing temperatures that will cause significant protein damage and induce a heat-shock response for about 30 days each summer.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.270

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)0.0000.000

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 head0.220
Teacher spread0.210 · 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 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

Citations131
Published2002
Admission routes1
Has abstractyes

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