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Record W2050010137 · doi:10.5539/jas.v3n3p111

Vascular Plant Composition and Diversity of a Coastal Hill Forest in Perak, Malaysia

2011· article· en· W2050010137 on OpenAlexvenueno aff
S. Ghollasimood, I. Faridah Hanum, M. Nazre, Abd Kudus Kamziah, A. G. Awang Noor

Bibliographic record

VenueJournal of Agricultural Science · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant and Fungal Species Descriptions
Canadian institutionsnot available
Fundersnot available
KeywordsSpecies richnessLianaDipterocarpaceaeSpecies diversityBiologyFloristicsVascular plantDominance (genetics)Diversity indexBotanyRubiaceaePteridophyteEcology

Abstract

fetched live from OpenAlex

Vascular plant species and diversity of a coastal hill forest in Sungai Pinang Permanent Forest Reserve in Pulau Pangkor at Perak were studied based on the data from five one hectare plots. All vascular plants were enumerated and identified. Importance value index (IVI) was computed to characterize the floristic composition. To capture different aspects of species diversity, we considered five different indices. The mean stem density was 7585 stems per ha. In total 36797 vascular plants representing 348 species belong to 227 genera in 89 families were identified within 5-ha of a coastal hill forest that is comprises 4.2% species, 10.7% genera and 34.7% families of the total taxa found in Peninsular Malaysia. Based on IVI, Agrostistachys longifolia (IVI 1245), Eugeissona tristis (IVI 890), Calophyllum wallichianum (IVI 807), followed by Taenitis blechnoides (IVI 784) were the most dominant species. The most speciose rich families were Rubiaceae having 27 species, followed by Dipterocarpaceae (21 species), Euphorbiaceae (20 species) and Palmae (14 species). According to growth forms, 57% of all species were trees, 13% shrubs, 10% herbs, 9% lianas, 4% palms, 3.5% climbers and 3% ferns. Diversity indices were higher along the stream side and species accumulation curve showed sampling area captured a high proportion of the species richness.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.111

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.014
GPT teacher head0.184
Teacher spread0.170 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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