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Record W3007414286

Effect of Dietary Protein on Growth, Feed Utilization and Body Composition of Silver Catfish Schilbe intermedius Rüppel, 1832 Fingerlings

2020· article· en· W3007414286 on OpenAlexvenueno aff
Comlan Ephrem Tossavi, Arnauld Sèdjro Martin Djissou, Nahoua Issa Ouattara, Emile Didier Fiogbé, Jean‐Claude Micha

Bibliographic record

VenueInternational Journal of Aquaculture · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsCatfishBiologyDietary proteinFish mealAnimal scienceFeed conversion ratioNutrientComposition (language)MealCaseinProtein efficiency ratioDry matterWeight gainFood scienceBody weightFish <Actinopterygii>FisheryEcologyEndocrinology
DOInot available

Abstract

fetched live from OpenAlex

The captive breeding of the Silver catfish Schilbe intermedius was envisaged to promote the aquaculture and to reduce the overfishing of this endangered species. S. intermedius fingerlings were fed various dietary protein levels to investigate their growth performance, feed utilization and carcass proximate composition. Fish meal and casein were the sources of protein used in the study. Six isocaloric experimental diets containing 25%~60% crud protein (CP) diet have been  fed to three-replicate six groups of forty (40) fishes (mean weight: 1.640 ± 0.015 g) for 8 weeks. Both percent weight gain and feed efficiency ratio significantly increased with increasing dietary protein levels up to 45%, while there were no significant differences for protein levels from 45 to 60%. Growth performances and nutrient utilization parameters of fingerlings fed different diets varied significantly (P 0.05) with that of fish fed 60% dietary protein. Lipid content increased with increasing dietary protein levels. The dry matter and protein content of the initial sample were significantly higher (P > 0.05) than the values after feeding the fish with experimental diets.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.255

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 head0.016
GPT teacher head0.250
Teacher spread0.234 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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