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Record W4391948281 · doi:10.1093/jme/tjad172

Highlights of Veterinary Entomology, 2022

2024· article· en· W4391948281 on OpenAlexaboutno aff
Caleb B Hubbard

Bibliographic record

VenueJournal of Medical Entomology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyEntomologyVeterinary medicineVeterinary parasitologyZoologyImmunology

Abstract

fetched live from OpenAlex

The field of veterinary entomology is dominated by research concerning insects and arthropods that negatively impact the health of domestic animals. A curated selection of peer-reviewed research which was highlighted at the 2022 Joint Meeting between the Entomological Society of America, the Entomological Society of Canada, and the Entomological Society of British Columbia, which prioritized exploring entomology through the lens of art, science, and culture are summarized. Articles are categorized into (i) biting and non-biting flies, (ii) ectoparasites, and (iii) review articles, with the overall goal of showcasing innovative methodologies, addressing overlooked questions in veterinary entomology, and highlighting comprehensive reviews. While not exhaustive, the selected studies represent a myriad of arthropods, methodologies, and perspectives, to inspire future scientists with diverse research avenues and emphasize the continual evolution and importance of entomological studies in today's 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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0480.000

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 head0.043
GPT teacher head0.340
Teacher spread0.296 · 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.

Study designNot applicable
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

Citations1
Published2024
Admission routes1
Has abstractyes

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