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Record W4387063419 · doi:10.1016/j.oneear.2023.08.025

Insights from citizen science reveal priority areas for conserving biodiversity in Bangladesh

2023· article· en· W4387063419 on OpenAlexafffundabout
Shawan Chowdhury, Richard A. Fuller, Md. Rokonuzzaman, Shofiul Alam, Priyanka Das, Asma Siddika, Sultan Ahmed, Mahzabin Muzahid Labi, Sayam U. Chowdhury, Sharif A. Mukul, Monika Böhm, Jeffrey O. Hanson

Bibliographic record

VenueOne Earth · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsCarleton University
FundersDeutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-LeipzigNational Cancer CenterUniversity of QueenslandDeutsche ForschungsgemeinschaftEnvironment and Climate Change CanadaNature Conservancy of CanadaAustralian Government
KeywordsCitizen scienceBiodiversityEnvironmental planningEnvironmental resource managementGeographyEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

The tropics contain a vast majority of species, yet our understanding of tropical biodiversity is limited. Here we combine species locality data from scientific databases and social media to examine the coverage of species by existing protected areas in Bangladesh and identify priority areas for future expansion. Although protected areas cover 4.6% of Bangladesh, only five species (0.004% of 1,097 species) are adequately represented, and 22 species are entirely absent from the existing protected-area system, including seven threatened species. Our spatial prioritization identified priority areas comprising 39% of Bangladesh, mainly in the northeast and southeast. The most irreplaceable areas (top 10%) are in hill forests and, to a lesser extent, agricultural landscapes. Our findings inform conservation policies for the Bangladesh government in order to meet the Kunming-Montreal Global Biodiversity Framework targets. In general, the approach can be broadly applicable to countries with limited data in global biodiversity repositories.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.040
GPT teacher head0.244
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; both teacher heads agree on what is shown here.

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

Citations21
Published2023
Admission routes3
Has abstractyes

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