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Record W3025902479 · doi:10.5539/sar.v9n3p30

Socio-economic Benefits of Non-timber Forest Products to the AFCOE2M Communities of Southern Cameroon

2020· article· en· W3025902479 on OpenAlexvenueno aff
Roseline Gusua Caspa, Gwendoline Nyambi, Mbang J. Amang, M. N. Mabe, A. B. Nwegueh, Bernard Foahom

Bibliographic record

VenueSustainable Agriculture Research · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLivelihoodGarcinia kolaGeographyAgroforestryEthnobotanyBiologyMedicinal plantsBotanyAgriculture

Abstract

fetched live from OpenAlex

A study was carried out in the community forest of Ebo, Medjounou and Mbamesoban communities (AFCOE2M) in the South Region of Cameroon to evaluate the contribution of non-timber forest products (NTFPs) to the people’s livelihood. The study identifies the various NTFPs used and further evaluates their socio-economic and cultural contributions in sustaining the livelihood of the AFCOE2M community. Essentially, the study assesses the exploitation and utilization of NTFPs. One Hundred and twenty five (125) individuals were randomly selected in the three villages that make up the AFCOE2M community forest. Fifty two (52) species of NTFPs of plant origin were identified, from which seven (7) were frequently used in all the three villages namely; Irvingia gabonensis, Trichoscypha acuminata, Alstonia boonei, Garcinia kola, Piper guineense, Picralima nitida, and Ricinodendron heudelotii. Results reveal that NTFPs plant parts used for consumption consist of 68% fruits, 20% seeds, 5% barks, 4% roots and 3% leaves. NTFPs used for medicinal purposes comprised of 70% barks, 16% seeds, 7% leaves, 5% fruits and 2% roots. There was a significant difference (P = 0.049) in the number of NTFP types consumed as food while that was not the case for medicinal NTFPs (P = 0.86). There was a significant difference in the number of NTFP species used for food originating from different land use types (P-value = 0.048) as well as between those used for medicinal purposes (P-value = 0.012).

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.605

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.044
GPT teacher head0.267
Teacher spread0.223 · 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 designQualitative
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

Citations9
Published2020
Admission routes1
Has abstractyes

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