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Record W3111000261 · doi:10.4314/ijbcs.v14i8.15

Étude ethnobotanique des plantes alimentaires utilisées en médecine traditionnelle dans la région Maritime du Togo

2020· article· fr· W3111000261 on OpenAlexaff
Stéphane Effoe, Efui Holaly Gbekley, Mamatchi Mélila, Amégninou Aban, Tchadjobo Tchacondo, Elolo Osseyi, Damintoti Simplice Karou, Kouami Kokou

Bibliographic record

VenueInternational Journal of Biological and Chemical Sciences · 2020
Typearticle
Languagefr
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsMinistère des Ressources naturelles et des Forêts (Québec)
Fundersnot available
KeywordsGynecologyTraditional medicineHumanitiesMedicineArt

Abstract

fetched live from OpenAlex

Les plantes alimentaires contiennent des principes actifs doués de diverses propriétés médicinales pouvant intervenir dans le traitement de nombreuses maladies. Cette étude est consacrée au recensement des plantes ayant des potentiels nutritionnel et thérapeutique dans la région Maritime du Togo, dans le but de la valorisation de ces plantes. De juin à septembre 2017, une enquête ethnobotanique, basée sur l’utilisation des interviews individuelles à l'aide d'un questionnaire semi-structuré, a été réalisée auprès de 101 Praticiens de la Médecine Traditionnelle. Au total 86 espèces végétales appartenant à 72 genres et 36 familles ont été identifiées. Les Fabaceae et les Solanaceae (7 espèces chacune) ont été les plus représentées. Les espèces les plus citées ont été Ocimum gratissimum L. (10,48%), Vernonia amygdalina Delile (6,71%), Lactuca taraxacifolia (Willd.) Schum. (6,08%) et Heliotropium indicum L. (5,66%). Les feuilles (77,85%), les fruits (5,63%) et les racines (4,26%) sont les organes les plus utilisées sur 799 recettes inventoriées. La principale forme galénique reste la sauce (51,19%) et le mode principal d’administration est la voie orale (90,74%). Concernant les maladies traitées, les affections du tube digestif sont au premier rang (43,80%) suivies par des affections cardiovasculaires (13,52%). Cette étude fournie une base de données sur des plantes ayant des potentiels nutritionnel et thérapeutique au Togo.Mots clés : Alicaments, potentiels nutritionnel et thérapeutique, sécurité alimentaire, Togo. English title: Ethnobotanical study of some food plants used in traditional medicine in the Maritime region of TogoFood plants contain active substances with various medicinal properties that can be used to treat many diseases. This study is devoted to the inventory of plants with nutritional and therapeutic potential in Maritime region of Togo, with the aim of promoting these plants. From June to September 2017, an ethnobotanical survey was conducted among 101 Traditional Medicine Practitioners through individual interviews using a semi-structured questionnaire. A total of 86 plants species belonging to 72 genera and 36 families were identified. Fabaceae and Solanaceae (7 species each) were the most represented. The most cited species were Ocimum gratissimum L. (10.48%), Vernonia amygdalina Delile (6.71%), Lactuca taraxacifolia (Willd.) Schum. (6.08%) and Heliotropium indicum L. (5.66%). The leaves (77.85%), fruits (5.63%) and roots (4.26%) were the most plant parts used out of 799 inventoried recipes. The main dosage form remains the sauce (51.19%) and the main mode of administration is the oral route (90.74%). Regarding the treated diseases, the digestive disorders are in first place (43.80%), followed by cardiovascular diseases (13.52%). This study provides a database of plants with nutritional and therapeutic potential in Togo.Keywords: Food plants, nutritional and therapeutic potentials, food security, Togo.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.037
GPT teacher head0.249
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations14
Published2020
Admission routes1
Has abstractyes

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