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Record W2135245356 · doi:10.1186/1746-4269-2-31

Ethnoveterinary medicines used for horses in Trinidad and in British Columbia, Canada

2006· article· en· W2135245356 on OpenAlexafffundabout
Cheryl Lans, Nancy J. Turner, Gerhard W. Brauer, Grant Lourenco, Karla Georges

Bibliographic record

VenueJournal of Ethnobiology and Ethnomedicine · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEthnobotanical and Medicinal Plants Studies
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Victoria
KeywordsGeographyMedicinal plantsEthnobotanyTraditional medicineSocioeconomicsMedicine

Abstract

fetched live from OpenAlex

This paper investigates the commonalities in ethnoveterinary medicine used for horses between Trinidad (West Indies) and British Columbia (Canada). These research areas are part of a common market in pharmaceuticals and are both involved in the North American racing circuit. There has been very little research conducted on medicinal plants used for horses although their use is widespread. The data on ethnoveterinary medicines used for horses was obtained through key informant interviews with horse owners, trainers, breeders, jockeys, grooms and animal care specialists in two research areas: Trinidad and British Columbia (BC). A participatory validation workshop was held in BC. An extensive literature review and botanical identification of the plants was also done. In all, 20 plants were found to be used in treating racehorses in Trinidad and 97 in BC. Of these the most-evidently effective plants 19 of the plants used in Trinidad and 66 of those used in BC are described and evaluated in this paper. Aloe vera, Curcuma longa and Ricinus communis are used in both research areas. More research is needed in Trinidad to identify plants that respondents claimed were used in the past. Far more studies have been conducted on the temperate and Chinese medicinal plants used in BC and therefore these ethnoveterinary remedies reflect stronger evidence of efficacy.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.241
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 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

Citations93
Published2006
Admission routes3
Has abstractyes

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