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Record W6910573522 · doi:10.48336/ehgt-9320

Low temperatures typical of winter cage-site conditions in Atlantic Canada impact the growth, physiology, health and welfare of Atlantic salmon (Salmo salar)

2023· article· en· W6910573522 on OpenAlexaffabout

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFish <Actinopterygii>Shock (circulatory)Heat stressEctothermFight-or-flight responseHsp70Heat shock proteinAquatic animal

Abstract

fetched live from OpenAlex

Limited research has been conducted on the physiology of Atlantic salmon (Salmo salar) at cold temperatures despite significant mortalities during the winter at sea-cages in Atlantic Canada. Thus, in this thesis, I exposed post-smolt cultured Atlantic salmon to a seasonal decline in temperature from 8 to 1°C (at 1°C week-1), and a ‘cold-shock’ from 3 to 0°C for 4 or 24 hours. During the seasonal decline in temperature, feeding decreased starting at 6°C (and ceased by 1-2°C), osmoregulatory changes and increases in heat shock protein expression began at 4-5°C, and at 1°C elevated plasma cortisol levels indicative of mild stress were measured. The ‘cold-shocks’ resulted in a relatively small stress response (i.e., increases in plasma cortisol and glucose), but no other adverse effects or mortalities. Nonetheless, a number of mortalities/moribund fish were noted when various groups of Atlantic salmon were held for long periods at < 8°C. Moribund fish were lethargic and swam erratically, had enlarged livers and plasma enzymes suggestive of liver damage, and developed ulcers to the head/jaw. The former symptoms are indicative of ‘Winter Syndrome’ described in gilthead sea bream (Sparus aurata) aquaculture, and these ulcers have previously been observed in Norway and associated with tenacibaculosis.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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 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

Citations1
Published2023
Admission routes2
Has abstractyes

Explore more

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