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Record W1485957176 · doi:10.1016/j.jep.2015.07.025

Ethnobotanical study of medicinal plants by population of Valley of Juruena Region, Legal Amazon, Mato Grosso, Brazil

2015· article· en· W1485957176 on OpenAlexaff
Isanete Geraldini Costa Bieski, Marco Leonti, John T. Arnason, Jonathan Ferrier, Michel Rapinski, Ivana Maria Póvoa Violante, Sikiru Olaitan Balogun, Joao Filipe Costa Alves PEREIRA, Rita de Cassia Feguri Figueiredo, Célia Regina Lopes, Dennis Rodrigues da Silva, Aloir Pacini, Ulysses Paulino Albuquerque, Domingos Tabajara de Oliveira Martins

Bibliographic record

VenueJournal of Ethnopharmacology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytochemistry Medicinal Plant Applications
Canadian institutionsUniversity of Ottawa
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de Mato GrossoInstituto Nacional de Ciência e Tecnologia em Áreas Umidas
KeywordsEthnobotanyMedicinal plantsTraditional medicineLamiaceaeFabaceaeAmazon rainforestGeographyPopulationTraditional knowledgeBioprospectingMelissa officinalisPlant speciesHabitBiologyMedicineEcologyEnvironmental healthIndigenous

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.046
GPT teacher head0.328
Teacher spread0.282 · 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".

Quick stats

Citations204
Published2015
Admission routes1
Has abstractno

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