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Growth and feed utilization of large size rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) reared in freshwater: diet and species effects, and responses over time

2004· article· en· W2148961185 on OpenAlexaff
Paula Azevedo, S. Leeson, C.Y. Cho, Dominique Bureau

Bibliographic record

VenueAquaculture Nutrition · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsRainbow troutSalmoBiologyAnimal scienceFeed conversion ratioTroutNutrientWeight gainSalmonidaeFisheryBrown troutFish <Actinopterygii>Body weightEcologyEndocrinology

Abstract

fetched live from OpenAlex

Four diets differing in crude protein/crude lipid concentrations (CP/CL), 570/200, 510/220, 460/240, 430/260 (g kg−1 dry diet) were fed to near-satiety to rainbow trout (initial body weight, IBW = 268 g, domestic strain) and Atlantic salmon (IBW = 456 g, wild strain) for 308 days to determine the effect of diets, species and fish size on efficiency of feed, nitrogen (N) and energy utilization. Within species, weight gain, feed efficiency (FE), and energy retention efficiency (ERE, E gain/E intake) were not affected by diet (P < 0.05). N retention efficiency (NRE, N gain/N intake) increased linearly (P < 0.05) with decreasing CP/CL for both species. NRE and FE were higher for salmon compared with trout. Whole body CP of salmon was significantly (P < 0.05) higher, and lipid significantly (P < 0.05) lower, than those of trout. There was a significant (P < 0.05) linear decrease in FE as fish grew, regardless of diet and species. NRE linearly decreased (P < 0.0001) and lipid to protein deposition ratio (LD/PD) increased (P < 0.05) as trout grew. NRE linearly increased (P < 0.05) while ERE and LD/PD linearly decreased (P < 0.05) as salmon grew. Salmon appeared to rely to a greater extent on non-protein energy-yielding nutrients to support cost of growth than did trout.

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 score0.900
Threshold uncertainty score0.508

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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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

Citations84
Published2004
Admission routes1
Has abstractyes

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