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Record W3011585543 · doi:10.1155/2020/4878169

Morphological Variability of <i>Pterocarpus erinaceus</i> Poir. in Togo

2020· article· en· W3011585543 on OpenAlexfundno aff
Benziwa Nathalie Johnson, Marie Luce Akossiwoa Quashie, Kossi Adjonou, Kossi Novinyo Segla, Adzo Dzifa Kokutsè, Kouami Kokou

Bibliographic record

VenueInternational Journal of Forestry Research · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsFabaceaeGeographyErinaceusPrincipal component analysisForestryPopulationBiologyBotanyMathematicsStatisticsDemography

Abstract

fetched live from OpenAlex

Pterocarpus erinaceus Poir. (Fabaceae), also called Vène or West African rosewood, is a multipurpose endemic forest species of Sahelo-Sudanian and Sudano-Guinean savannas and forests of West Africa. In Togo, the species is overexploited, which dangerously hinders its survival. The need and emergency of restoring declining stands, using seeds, or propagating material suggests an assessment of its morphological variability. The purpose of this study is to identify the discriminating morphological descriptors, allowing us to describe and also to characterize the species. Five provenances distributed over the whole geographical distribution area in Togo were evaluated for leaf (7 descriptors), fruit (4 descriptors), and seed (4 descriptors) traits. The coefficient of variation (CV) and the principal component analysis (PCA) are used to assess the variability among tree populations. Results show that the discriminating morphological descriptors for P. erinaceus in Togo are the width of the leaf and the terminal leaflet, the length and the width of the fruit, and length and the weight of the seed. These six main relevant variables allow us to discriminate three morphological groups of P. erinaceus population.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.094
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
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 head0.107
GPT teacher head0.346
Teacher spread0.238 · 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 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

Citations7
Published2020
Admission routes1
Has abstractyes

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