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Record W2297616818 · doi:10.1139/gen-2015-0159

Diversity and human perceptions of bees (Hymenoptera: Apoidea) in Southeast Asian megacities

2016· article· en· W2297616818 on OpenAlexvenueno aff
Kong‐Wah Sing, Wenzhi Wang, Tao Wan, Ping-Shin Lee, Zongxu Li, Xing Chen, Yunyu Wang, John‐James Wilson

Bibliographic record

VenueGenome · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsApoideaSpecies richnessMegacityUrbanizationEcologyGeographyBiodiversityBiologyAbundance (ecology)Hymenoptera

Abstract

fetched live from OpenAlex

Urbanization requires the conversion of natural land cover to cover with human-constructed elements and is considered a major threat to biodiversity. Bee populations, globally, are under threat; however, the effect of rapid urban expansion in Southeast Asia on bee diversity has not been investigated. Given the pressing issues of bee conservation and urbanization in Southeast Asia, coupled with complex factors surrounding human-bee coexistence, we investigated bee diversity and human perceptions of bees in four megacities. We sampled bees and conducted questionnaires at three different site types in each megacity: a botanical garden, central business district, and peripheral suburban areas. Overall, the mean species richness and abundance of bees were significantly higher in peripheral suburban areas than central business districts; however, there were no significant differences in the mean species richness and abundance between botanical gardens and peripheral suburban areas or botanical gardens and central business districts. Urban residents were unlikely to have seen bees but agreed that bees have a right to exist in their natural environment. Residents who did notice and interact with bees, even though being stung, were more likely to have positive opinions towards the presence of bees in cities.

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 score0.535
Threshold uncertainty score0.359

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 head0.035
GPT teacher head0.198
Teacher spread0.163 · 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

Citations28
Published2016
Admission routes1
Has abstractyes

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