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Record W3091180833 · doi:10.3329/bjz.v48i1.47877

Species Diversity, Distribution and Relative Abundance of Avifuana in the Mangrove of Karamjal Forest Station, Sundarbans

2020· article· en· W3091180833 on OpenAlexaff
Humayra Mahmud, Animesh Goshe Ayon, Md. Anwarul Islam

Bibliographic record

VenueBangladesh Journal of Zoology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsSpecies evennessAbundance (ecology)Relative species abundanceEndangered speciesMangroveEcologyHabitatSpecies diversityGeographyBiologyForestry

Abstract

fetched live from OpenAlex

Study on bird species diversity, distribution and relative abundance is important for conservation efforts in local and national scale. However, bird diversity, distribution and relative abundance are little known in Karamjal Forest Station, Sundarbans. Ecological appraisal of bird species diversity, distribution and relative abundance of the avifauna of the Karamjal Forest Station were conducted from June 2015 to April 2016. A total of 156 bird species was recorded during the study period. Of which, one was globally Critically Endangered, Gyps bengalensis and two were Near Threatened i.e., Gyps himalayensis and Lusciniapectardens. The distribution of bird among habitat type was significantly different (f =22.069, p<0.05, df = 2). Walking trail was inhabited the highest species diversity (H’= 3.77) with the highest evenness (J=0.823) while water body was recorded the lowest species diversity (H’= 2.93) with the lowest evenness (J=0.804), it could have a relation to the availability of food items in the habitat. This study showed that despite huge tourist pressures this forest station harbour diverse avian species and thus this area should be managed in order to enhance the population of avian species.
 Bangladesh J. Zool. 48(1): 67-79, 2020

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.999

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)0.0020.000

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 head0.030
GPT teacher head0.234
Teacher spread0.204 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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