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Record W2883252677 · doi:10.1111/are.13782

Determination of optimal temperature(s) in juvenile red‐spotted grouper<i>Epinephelus</i><i>akaara</i>(Temminck &amp; Schlegel) based on growth performance and stress responses

2018· article· en· W2883252677 on OpenAlexfundno aff
Jang‐Won Lee, Hea Ja Baek

Bibliographic record

VenueAquaculture Research · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
FundersMinistry of Oceans and FisheriesU.S. Forest ServiceRural Development AdministrationMinistry of Agriculture - Saskatchewan
KeywordsGrouperBiologyJuvenileAnimal scienceEpinephelusHeat shock proteinAquacultureHeat stressFisheryEndocrinologyFish <Actinopterygii>Internal medicineBiochemistryEcology

Abstract

fetched live from OpenAlex

This study sought to determine the optimal temperature(s) for aquaculture of juvenile red-spotted grouper Epinephelus akaara (Temminck & Schlegel) (mean initial BW: 3.1 g). Growth performance, insulin-like growth factor 1 (IGF-1) expression and thermal stress responses (plasma cortisol, glucose, and hepatic heat shock protein 60 expression) were evaluated at three constant temperatures (24°C, 26°C and 28°C) in a 2-week trial. At the end of the trial, final BW was significantly higher at 26°C and 28°C than at 24°C (p < 0.05); a quadratic regression analysis of final BW showed the optimum temperature for growth was 27.5°C (p < 0.05, R2 = 0.806). The highest hepatic IGF-1 expression was observed at 26°C (p < 0.05). On the other hand, hepatic heat shock protein 60 expression was highest at 28°C (p < 0.05), suggesting thermal stress. In conclusion, temperature optima, which support excellent growth but induce minimal thermal stress, was 26°C. This fine information within a narrow temperature range is expected to give empirical information for red-spotted grouper farmers to sustain maximal production efficiency with avoiding thermal stress and to determine the future location of production, especially in consideration of arising seawater temperatures.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.303
Teacher spread0.265 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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