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Record W2497548930 · doi:10.5539/jas.v8n8p79

Performance of Nile Tilapia (Oreochromis niloticus) Fed Diets Containing Blood Meal as a Replacement of Fish Meal

2016· article· en· W2497548930 on OpenAlexvenueno aff
James G. Kirimi, Levi M. Musalia, Adiel Magana, Jonathan Munguti

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsNile tilapiaOreochromisFish mealAnimal scienceMealBiologyFeed conversion ratioBlood mealTilapiaWeight gainBody weightFood scienceFisheryFish <Actinopterygii>Endocrinology

Abstract

fetched live from OpenAlex

&lt;p&gt;A 100 days experiment was conducted to determine the effect of feeding blood meal (BM) as a replacement of fish meal (FM), as the main source of animal protein, on growth rate and economic performance of Nile tilapia in fertilized pond. Three isonitrogenous diets (35% crude protein) were formulated using either FM as the main source of animal protein (Diet 1); 50% replacement of FM with blood meal (Diet 2); or 100% replacement of FM with BM (Diet 3). Three hundred Nile tilapia fingerlings (12±3 g) were randomly distributed into three groups of four replicates of 25 fingerlings per cage. The groups were randomly assigned the 3 diets which were fed at 2% of their biomass at 10 am and 4 pm every day. Percentage daily weight gain (DWG), relative growth rate (RGR), specific growth rate (SGR), survival rate and feed utilisation efficiency were measured. Fish fed diet 1 were larger (50.69 g) (P &amp;lt; 0.05) than those fed diet 2 (48.47 g) and 3 (40.37 g). Replacement of FM with 50% and 100% BM reduced the incidence cost (45.55, 37.83 and 31.88, respectively). The profit index was highest with 100% (9.42) replacement of FM, compared to 50% (7.95) and 0% (6.69). Although replacing FM with BM was associated with reduced growth, the economic return was better (P &amp;lt; 0.05). Based on the present results, it was economical to use BM as a major protein source instead of FM in formulating fish feed.&lt;/p&gt;

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.014
GPT teacher head0.221
Teacher spread0.208 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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