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Record W2093691571 · doi:10.5539/ijb.v3n3p60

Assessment of Structural Diversity of Beech Forest Stands in North of Iran

2011· article· en· W2093691571 on OpenAlexvenueno aff
Vahab Sohrabi, Ramin Rahmani, M H Moayeri, Shahrokh Jabbari

Bibliographic record

VenueInternational Journal of Biology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBeechBiodiversitySpecies evennessGeographyGamma diversitySpecies diversityForestryDiversity (politics)Diversity indexGlobal biodiversityEcologyMathematicsSpecies richnessAlpha diversityBiology

Abstract

fetched live from OpenAlex

Stand structure is a key principle in stand biodiversity. High biodiversity was associated with the stands that have different trees species with different dimension. In this regard, for evaluation Structural diversity in different diameter and height classes and also their changing procedure of beech stands in north of Iran, 30 modified Whittaker plots by systematic random system were located. The heterogeneity indices of Shannon–Wiener, number of equally common species and evenness indices of Simpson and smith-Wilson were using for the quantitative data. In order to understand the diversity condition in horizontal and vertical composition of stand further, the diameter divided in 10-cm classes and method of Mohajer and the height divided in 10-m height classes and dominant height. Then diversity of each class was extract by ecological methodology software. Results showed the most diversity of trees and shrubs is in low height and diametrical classes. Thus, the study of biodiversity changes in different diameter and height category cause ecologically precise perspective in management of forest stands.

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)0.001
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.265
Teacher spread0.218 · 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
Published2011
Admission routes1
Has abstractyes

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