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Record W4416959140 · doi:10.1038/s41598-025-29578-y

Effects of supplementation Senegalia macrostachya seed flour on the zootechnical performance of traditional chickens from Burkina Faso

2025· article· en· W4416959140 on OpenAlexaff
Hadidjatou Belèm, Windmi Kagambéga, Eliasse Zongo, Assétou Sawadogo, Cyprial Ndumiso Ncobela, Marie-Pierre Létourneau-Montminy, Muhammad Abdul Rahim, N. Méda

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLivestock and Poultry Management
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAcaciaGum arabicMealFeed conversion ratioLegume

Abstract

fetched live from OpenAlex

The decline in productivity of traditional chicken in Burkina Faso is mainly linked to the management of its feeding. This work aimed to evaluate the acceptability of Senegalia macrostchya seed flour in the feed ration of traditional chickens. This study involved 50 traditional chicks aged eight weeks. The chickens were divided into five batches of ten chicks corresponding to the five experimental diets containing 0%, 1%, 2%, 3% and 4% Senegalia macrostachya seed meal. The nutritional composition of Senegalia macrostachya seed meal, growth parameters and carcass characteristics were determined. Senegalia macrostachya seeds were mainly rich in protein (40.3 ± 0.74% DM) and fat (27 ± 6.45% DM). These seeds also contain mineral salts with particularity for calcium (0.41 ± 0.00% DM). In addition, Senegalia macrostachya seeds were rich in palmitic acid, α-linolenic acid, linoleic acid, and oleic acid. Thus, the group of chickens fed with 3% supplementation of Senegalia macrostachya seed meal had a feed conversion ratio (4.61), individual feed consumption (42.56 g/d) and average daily gain (11.29 g/d) higher than the other supplementation levels (0.1, 2 and 4%). However, this supplementation resulted in a decrease in abdominal fat of the batches of 1%, 2%, 3% and 4% compared to the 0% control batch. Senegalia macrostachya seed flour could be a solution to the protein source problem in traditional chicks.

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 score0.559
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.220
Teacher spread0.206 · 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
Published2025
Admission routes1
Has abstractyes

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