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Record W2075483605 · doi:10.4000/vertigo.13929

Valorisation de la chenille comestible Bunaeopsis aurantiaca dans la gestion communautaire des forêts du Sud-Kivu (République Démocratique du Congo)

2013· article· fr· W2075483605 on OpenAlexvenueno aff
Uwikunda Serondo Héritier, Mande Paul, Alunga Lufungula Georges, Balagizi Karhagomba Innocent, Isumbisho Mwapu Pascal

Bibliographic record

VenueVertigO · 2013
Typearticle
Languagefr
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsForestryBiologyGeography

Abstract

fetched live from OpenAlex

Cette étude visait à évaluer la valeur nutritive de la chenille Bunaeopsis aurantiaca, largement consommée par les populations de la forêt du bassin du Congo en général et du Sud-Kivu en particulier. Cette chenille a comme plante-hôte l’arbre Uapaca guineensis, un arbre connu pour ses planches en bois de qualité et comme un arbre à chenille de valeur. Les résultats de l’analyse immédiate de sa composition en matières nutritives ont révélé que ces chenilles sont constituées de 49 % de protéines brutes, 24,2 % de matières grasses, 4,5 % de sucres et 3,2 % de matières minérales totales dans leur poids sec. La valeur énergétique de 100 g de matières sèches de cet aliment a été évaluée à 433 kcal. Cette valeur nutritive est comparable à celle du poisson, en l’occurrence la sardine très appréciée localement, la Limnothrissa miodon. Les résultats de cette étude démontrent à suffisance que la chenille B. aurantiaca, tout comme d’autres chenilles comestibles, constitue une source riche en protéines animales et est donc très appréciable. Il s’agit d’un produit forestier non ligneux de grande valeur dans la sécurité alimentaire et qui exige une attention plus accrue de toute la communauté afin de garantir sa gestion durable et de maintenir les différents services environnementaux de la forêt.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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.

Study designObservational
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

Citations10
Published2013
Admission routes1
Has abstractyes

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Same venueVertigOSame topicAfrican Botany and Ecology StudiesFrench-language works237,207