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Record W4376506982 · doi:10.56588/iabcd.v2i1.153

ANGIOSPERMIC MEDICINAL PLANTS DIVERSITY OF GRAMBHARTI (AMARAPUR) VILLAGE, MANSA TALUKA, GANDHINAGAR DISTRICT, GUJARAT, INDIA

2023· article· en· W4376506982 on OpenAlexaff
Hemu Damor, Hirali Patel, Hitesh Solanki

Bibliographic record

VenueInternational Association of Biologicals and Computational Digest · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEthnobotanical and Medicinal Plants Studies
Canadian institutionsImpact
Fundersnot available
KeywordsHerbariumMedicinal plantsPlant diversityDiversity (politics)Field surveyGeographyEthnobotanyPlant speciesAgroforestryBiologyTraditional medicineBotanyMedicineSociologyCartography

Abstract

fetched live from OpenAlex

The present study deals with the diversity of Angiospermic medicinal plants of Grambharti (Amarapur) village, in Gandhinagar district during January 2023 to March 2023. Information regarding medicinal uses and local name of plants was collected through direct field survey and personal interview with the locals and knowledgeable persons and cultivators. Then identify the plant species and arranged according to Bentham and hooker’s classification system and prepare herbarium sheets.A total 96 Angiosperm plant species belonging to 46 families are recorded, in which 38 species belonging to 23 families are medicinal plant species. They are used in different disease. The study indicates that the area is very rich in traditional knowledge and a great diversity of medicinal plants which are offer a convenient strategy for promoting cultivation and conservation of variety of Angiosperm medicinal plants.

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)0.000
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.020
GPT teacher head0.243
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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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