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Changes in carabid beetle assemblages across an urban‐rural gradient in Japan

2003· article· en· W2165695286 on OpenAlexaboutno aff
Masahiro Ishitani, D. Johan Kotze, Jari Niemelä

Bibliographic record

VenueEcography · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeneralist and specialist speciesUrbanizationSpecies richnessEcologyGeographyAbundance (ecology)Rural areaBiologyHabitat

Abstract

fetched live from OpenAlex

As part of the international Globenet project, carabid beetles (Coleoptera, Carabidae) were collected using pitfall traps from four urban, four suburban and four rural sites in Hiroshima City, Japan, during the 2001 summer season. In agreement with expectation, carabid abundance and species richness decreased significantly from rural to urban sites. Furthermore, no large, and only few individuals of medium‐sized specialist species were collected from the urban environment, while many specimens of medium‐sized and some large‐sized specialist species were collected from the suburban and rural sites. Hiroshima city was characterised by medium‐sized generalist carabids, while the suburbs and the rural environments were characterised by small‐sized generalist beetles. These results did not apply at the species level. To summarise, we found a significant effect of urbanisation on the composition of carabid beetle assemblages in Hiroshima City. These changes were similar to those found in previous studies performed in Sofia (Bulgaria), Edmonton (Canada) and Helsinki (Finland). Thus, it appears that urbanisation has some similar and predictable effects on carabid assemblages in various parts of the world.

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)0.000
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 score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.033
GPT teacher head0.231
Teacher spread0.198 · 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

Citations126
Published2003
Admission routes1
Has abstractyes

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