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Record W1762972613 · doi:10.22092/ijfs.2018.114781

Effect of feeding rate on nutrient digestibility in Atlantic salmon, Salmo salar L.

2008· article· en· W1762972613 on OpenAlexaboutno aff
Mir Masoud Sajjadi, CG Carter

Bibliographic record

VenueUTAS Research Repository · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsDry matterSalmoAnimal sciencePhosphorusBiologyNutrientFecesProtein digestibilityFish <Actinopterygii>ChemistryFisheryEcology

Abstract

fetched live from OpenAlex

A digestibility trial was conducted to examine the effect of feeding rate on dry matter, gross energy, crude protein and phosphorus digestibility in Atlantic salmon (Salmo salar). Duplicate groups of fish were fed 0.25, 0.5, 0.75, 1.0, 1.25 and 1.9% BW/day. The faeces were collected by Guelph-type collectors for five successive days. Dry matter, protein and phosphorus digestibility’s were all significantly (p<0.05) affected by feeding rate. Dry matter digestibility was significantly lower in fish fed 0.25% BW day in comparison with fish fed 0.5, 0.75 and 1% BW day. Protein digestibility was significantly lower in fish fed 0.25% BW/day in comparison with 1.25% BW day. Phosphorus digestibility was significantly lower in fish fed 0.25% BW/day in comparison with all other treatments except for 1%BW day. There were no significant differences for energy digestibility between fish fed with different amount of feed. The main effect was reduced digestibility at the lowest level of intake with no obvious relationship between feeding rate and digestibility above this amount. This was explained by a relatively higher loss of endogenous faecal nitrogen and phosphorus at sub-maintenance feeding.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.302
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2008
Admission routes1
Has abstractyes

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