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Record W2956119210 · doi:10.4039/tce.2019.29

Diversity patterns of necrocolous beetles (Coleoptera: Scarabaeidae, Silphidae, Trogidae) in<i>Agave tequilana</i>Weber (Asparagaceae) fields of different ages

2019· article· en· W2956119210 on OpenAlexaff
William Rodríguez, José Luís Navarrete-Heredia, Ramón Rodríguez-Macias, Guillermo Briceño-Félix, Miguel Vásquez‐Bolaños, Jan Klimaszewski

Bibliographic record

VenueThe Canadian Entomologist · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and soil sciences
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersNational Aeronautics and Space Administration
KeywordsScarabaeidaeAsparagaceaeBiologyDominance (genetics)AgaveEcologyAbundance (ecology)Botany

Abstract

fetched live from OpenAlex

Abstract The necrocolous Coleoptera (attracted to carrion) are important to maintain balance in insect communities and in the recycling of soil nutrients; however, there is scarce data on the species that occur inAgave tequilanaWeber (Asparagaceae) fields. The diversity patterns of beetles (Coleoptera: Scarabaeidae, Silphidae, and Trogidae) dwelling in 2–4-year-oldA. tequilanaplots were established by sampling specimens from May to November 2016. In total, 5509 individuals of 23 species were collected. The highest species diversity was found in Magdalena Municipality, followed by the Arandas and Tequila municipalities (Jalisco, Mexico), with significant differences in abundance among municipalities and crop age. The variability in Magdalena and Tequila assemblages was associated with the temperature, while in Arandas it was attributed to the precipitation. The beetle species diversity, species replacement, and dominance in different municipalities are the result of changes in habitat, the interaction of environmental variables, distribution affinities of species, and agronomic practices inA.tequilanafields.

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 score0.029
Threshold uncertainty score0.059

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.196
Teacher spread0.180 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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