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Record W3111635543 · doi:10.1111/jwas.12759

Influence of temperature on growth and survival of juvenile winter flounder, <i>Pseudopleuronectes americanus</i> (Walbaum) reared under continuous light

2020· article· en· W3111635543 on OpenAlexaff
Paul Casey, Vahid Zadmajid, Ian A.E. Butts, Sune Riis Sørensen, Matthew K. Litvak

Bibliographic record

VenueJournal of the World Aquaculture Society · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsMount Allison UniversityGovernment of New BrunswickUniversity of New Brunswick
Fundersnot available
KeywordsWinter flounderFlatfishBiologyFlounderJuvenileFisheryAquacultureAnimal scienceFish <Actinopterygii>PleuronectidaeStockingGrowth rateZoologyEcology

Abstract

fetched live from OpenAlex

Abstract Winter flounder, Pseudopleuronectes americanus , has emerged as a promising candidate flatfish for cold‐water aquaculture and restocking. Here, juveniles were reared for 8 weeks at three temperatures: 10, 15, and 20°C under 24‐hr light. All fish were imaged at stocking and at 2‐week intervals, where growth was measured as changes in standard length ( S L ) and body area ( B A ). By week 2, fish reared at 15 and 20°C were larger than those grown at 10°C. At weeks 4 to 6, fish at 15°C were larger than fish at 20°C. Linear regressions were used to model growth dynamics over time at each temperature. Highly significant linear growth trajectories were detected over time for S L and B A . S L and B A regressions also showed a significant difference among the slopes across temperatures, where comparing slopes showed the best temperature to rear the flounder was 15°C. Weights of fish held at 15°C and 20°C were greater than at 10°C at the termination of the experiment. Within each temperature, the growth rate of malpigmented fish was not different from that of the normally pigmented fish. Overall, growth of winter flounder was comparable to that of other commercially produced flatfish species, providing strong evidence for this flatfish species as a potential species for aquaculture.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

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