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Record W2108997852 · doi:10.5376/ija.2015.05.0012

Effects of Different Feeding Rates on Growth Performance and Body Composition of Red Tilapia, <i>Oreochromis mossambiquse x O. niloticus</i>, Fingerlings

2015· article· en· W2108997852 on OpenAlexvenueno aff
El-Saidy D.M.S. Deyab, Ebtehal E. Hussein

Bibliographic record

VenueInternational Journal of Aquaculture · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsOreochromisTilapiaAnimal scienceBiologyFisheryComposition (language)Fish <Actinopterygii>Body weightFood scienceZoologyEndocrinology

Abstract

fetched live from OpenAlex

In aquaculture feeding rate is an important factor affecting the growth of fish and thus determining the optimal feeding rate is important to the success of any aquaculture operation. The aim of this study was to determine the optimal feeding rate for red  tilapia ( Oreochromis sp.). A 12-week feeding trial was conducted to examine the effects of different levels of the feeding rates on the growth performance and body composition of red tilapia, ( Oreochromis sp.). Fish of an average initial weight of 0.9±0.01 g were stocked in 12 glass aquaria (80 L each) at a rate of 15 fish per aquarium. All fish were fed the same diets contained 33.8% crude protein at a feeding rate of 1, 3, 5 and 7% of body weight daily. The results revealed that there was a significant differences in growth performance and feed utilization parameters with increasing feeding rates up to 5 % (P 0.05) by feeding rates. Lipid and ash contents were significantly (P < 0.05) influenced by feeding rates. These findings suggest that feeding rate of 5% of body weight daily can be considered as the optimal feeding rate for red  tilapia fingerlings which significantly enhance fish growth, feed utilization and body composition parameters.

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 score0.238
Threshold uncertainty score0.329

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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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