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Record W2013451267 · doi:10.1242/jeb.00707

Are hsps suitable for indicating stressed states in fish?

2003· review· en· W2013451267 on OpenAlexaff
George K. Iwama, Luis O.B. Afonso, Anne E. Todgham, Paige A. Ackerman, Kazumi Nakano

Bibliographic record

VenueJournal of Experimental Biology · 2003
Typereview
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of British ColumbiaInstitute for Marine Biosciences
Fundersnot available
KeywordsStressorFish <Actinopterygii>Context (archaeology)BiologyFight-or-flight responseHeat shock proteinCellular stress responseZoologyEcologyNeuroscienceGeneticsFishery

Abstract

fetched live from OpenAlex

In response to most stressors, fish will elicit a generalized physiological stress response, which involves the activation of the hypothalamic-pituitary-interrenal axis (HPI). As in other vertebrates, this generalized stress response comprises physiological responses that are common to a wide range of environmental, physical and biological stressors. Recently, several families of heat shock proteins (hsps) have been proposed as indicators of a generalized stress response at the cellular level. Recent findings that hsp levels, in various fish tissues, respond to a wide range of stressors have supported the use of these proteins as indicators of stressed states in fish. However, the cellular stress response can vary, for example, according to tissue, hsp family and type of stressor. This brief overview of these responses in fish asks the question of whether changes in levels and families of hsps can be used as a suitable indicator of stressed states in fish. By casting this question in the context of the well-established generalized physiological stress response in fish, we argue that the use of hsps as indicators of stressed states in fish in general is premature.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.066
GPT teacher head0.350
Teacher spread0.284 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations327
Published2003
Admission routes1
Has abstractyes

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