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The Effects of Two Rearing Salinities on Survival and Stress of Winter Flounder Broodstock

2002· article· en· W2036451090 on OpenAlexaff
Sébastien Plante, Céline Audet, Yvan Lambert, J. de la Noüe

Bibliographic record

VenueJournal of Aquatic Animal Health · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsUniversité LavalFisheries and Oceans CanadaUniversité du Québec à Rimouski
Fundersnot available
KeywordsBroodstockBiologyWinter flounderAnimal scienceCaptivitySalinityFlounderFisheryBrackish waterPhysiological conditionOsmoregulationAquacultureEcologyZoologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The goal of this study was to determine rearing conditions that would improve the survival of broodstock of winter flounder Pleuronectes americanus. We hypothesized that keeping wild winter flounder in iso-osmotic water would reduce the energy costs related to osmoregulation; the resulting energy gain could then be used for growth or immune responses. Eighty fish were randomly separated into four tanks, two containing seawater (SW; 28.7 ± 0.9‰ (mean ± SD)) and two containing brackish water (BW; 14.7 ± 1.7‰). Fish were sampled after 2 and 5 months of captivity for evaluation of their condition and stress status. Between the second and fifth months, the condition index increased significantly in both salinity groups, whereas body water content decreased. No salinity effect in terms of growth, condition, or energy reserves was found. However, the fish in BW showed much lower mortality. We found that the fish in SW had higher levels of the physiological indicators of stress than those in BW, which could have increased the risk of opportunistic infections in the former. Also, thrombocytes were absent in the SW fish after 2 months of captivity, which may have contributed to some mortalities. The lower resistance of certain opportunistic pathogens to BW is another possible explanation as to why fish in BW had lower occurrences of infectious diseases.

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 score0.678
Threshold uncertainty score0.097

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 studies0.0000.000
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 head0.037
GPT teacher head0.279
Teacher spread0.241 · 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

Citations14
Published2002
Admission routes1
Has abstractyes

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