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Record W2035225486 · doi:10.5539/ijb.v3n4p22

Relationship between Trees and Shrubs Biodiversity with Some Soil Physical Properties in Hyrcanian Forests (North of Iran)

2011· article· en· W2035225486 on OpenAlexvenueno aff
Vahab Sohrabi, H Habashi

Bibliographic record

VenueInternational Journal of Biology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Ecology and Soil Science
Canadian institutionsnot available
Fundersnot available
KeywordsSiltAltitude (triangle)Soil textureBiodiversityBulk densitySoil scienceVegetation (pathology)Environmental scienceRange (aeronautics)Soil testGeographyEcologyHydrology (agriculture)MathematicsGeologySoil waterBiologyGeomorphologyGeotechnical engineering

Abstract

fetched live from OpenAlex

In order to evaluation relationship between trees and shrubs biodiversity with some soil physical properties in hyrcanian forests, in range of 850-950 altitude from the sea level in 6 sites (in each site 30 plots) with least interfering of human in north aspect and with equal distance from each other were located. In this 20*50 meter frame, the characteristic of trees and shrubs species (Species name & diameter) recorded. The heterogeneity indices of simpson, shannon–wiener, simpson,s reciprocal, and number of equally common used for the quantitative data. In each plot, four soil samples (500g) taken in the depth ranges of 10–20-cm. After mixing its, one sample (200g) was selected for laboratory. Physical properties of used in research are soil texture (percent of sand, silt, and clay), percent of soil saturation and bulk density. Results showed sand percent have positive correlation in all sites except Carpineo-Paroteum type. In addition, results showed clay percent has negative correlation in all sites. Thus, according to special site condition in forest types, appropriate technique should be base on knowledge of qualitative and quantitative condition of communities.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.184

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 head0.049
GPT teacher head0.232
Teacher spread0.183 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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