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Record W4386945049 · doi:10.3201/eid2910.221899

One Health Approach to Globalizing, Accelerating, and Focusing Amphibian and Reptile Disease Research—Reflections and Opinions from the First Global Amphibian and Reptile Disease Conference

2023· article· en· W4386945049 on OpenAlexfundno aff
Matthew J. Gray, Robert J. Ossiboff, Lee Berger, Molly C. Bletz, Edward Davis Carter, Joseph A. DeMarchi, Leon Grayfer, David Lesbarrères, Daniel Malagon, An Martel, Debra L. Miller, Frank Pasmans, Lee F. Skerratt, Anastasia E. Towe, M. Wilber

Bibliographic record

VenueEmerging infectious diseases · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsnot available
FundersU.S. Geological SurveyDivision of Environmental BiologyTennessee Tech UniversityNature ConservancyEnvironment and Climate Change CanadaDivision of Integrative Organismal SystemsInstitute of Agriculture, University of TennesseeTennessee Wildlife Resources AgencyNational Science Foundation
KeywordsAmphibianBiodiversityDiseaseBiologyEcologyEnvironmental resource managementMedicine

Abstract

fetched live from OpenAlex

The world's reptiles and amphibians are experiencing dramatic and ongoing losses in biodiversity, changes that can have substantial effects on ecosystems and human health. In 2022, the first Global Amphibian and Reptile Disease Conference was held, using One Health as a guiding principle. The conference showcased knowledge on numerous reptile and amphibian pathogens from several standpoints, including epidemiology, host immune defenses, wild population effects, and mitigation. The conference also provided field experts the opportunity to discuss and identify the most urgent herpetofaunal disease research directions necessary to address current and future threats to reptile and amphibian biodiversity.

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)0.045
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.045
Threshold uncertainty score0.237

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.002
Science and technology studies0.0060.017
Scholarly communication0.0190.023
Open science0.0060.012
Research integrity0.0310.052
Insufficient payload (model declined to judge)0.0100.002

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 head0.074
GPT teacher head0.342
Teacher spread0.268 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations8
Published2023
Admission routes1
Has abstractyes

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