MétaCan
Menu
Back to cohort
Record W4213338582 · doi:10.1089/vbz.2021.0034

Integrated Approaches for the Identification of Mosquitoes (Diptera: Culicidae) from the Volcanoes of Central America Physiographic Subprovince of the State of Chiapas, Mexico

2022· article· en· W4213338582 on OpenAlexaff
Vicente Viveros-Santos, Luis M. Hernández‐Triana, Sergio Ibáñez‐Bernal, Aldo I. Ortega‐Morales, Nadya Nikolova, Pramual Pairot, Anthony R. Fooks, Mauricio Casas‐Martínez

Bibliographic record

VenueVector-Borne and Zoonotic Diseases · 2022
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDNA barcodingFaunaBiologyTaxonEcologyAnophelesZoologyGeographyMalaria

Abstract

fetched live from OpenAlex

Nowadays, there is a lack of information on the mosquito's fauna and DNA barcoding sequence reference library from many areas in Mexico, including the Volcanoes of Central America physiographic subprovince in the state of Chiapas. Consequently, a survey was undertaken to delineate the mosquito (Diptera: Culicidae) fauna in this region across different seasons using different collecting techniques. All species were identified by morphology and DNA barcoding, and their ecological features were also defined. In total, 62 taxa were morphologically examined, 60 of these were successfully identified based on morphological characteristics, but two were unable to be identified at the species level. The genera Aedes, Anopheles, Culex, and Wyeomyia are the most diverse among mosquito genera collected and include several species of medical and veterinary importance. Ecological characteristics of the immature habitats indicated that they were grouped into four categories namely, (1) large water bodies at ground level, (2) small and shady phytotelmata ( e.g. , tree holes and bamboo internodes), (3) large phytotelmata ( e.g. , plant leaves and axis bromeliad), and (4) artificial containers. The cytochrome c oxidase subunit I (COI) DNA barcoding sequences successfully separated the majority of these species, although specific species showed >2% intraspecific genetic divergences.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.391

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 studies0.0000.001
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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueVector-Borne and Zoonotic DiseasesSame topicMosquito-borne diseases and controlFrench-language works237,207