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Record W4377001024 · doi:10.37002/salve.ficha.20711

Ficha de Anomaloglossus baeobatrachus

2022· dataset· es· W4377001024 on OpenAlexaff
Rogério Pereira Bastos, Marcio Roberto Martins, Yeda Soares de Lucena Bataus, Lara Gomes Côrtes, Robson Vieira Guimarães Júnior, Juliana Rodrigues, Reuber Albuquerque Brandão, Miquéias Ferrão, Marcelo Gordo, Marinus Steven Hoogmoed, Ígor Luís Kaefer, Felipe Sá Fortes Leite, Natan Medeiros Maciel, Vitor Hugo Mendonça do Prado, Diego José Santana, Débora Leite Silvano, Moisés Barbosa de Souza, Luı́s Felipe Toledo

Bibliographic record

VenueDatasets - Sistema SALVE - ICMBio · 2022
Typedataset
Languagees
FieldHealth Professions
TopicOral and Craniofacial Lesions
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsBusiness administrationBusiness

Abstract

fetched live from OpenAlex

Última avaliação: 23/06/2017 Ano da publicação: 2023 Anomaloglossus baeobatrachus ocorre na região amazônica do Escudo das Guianas.No Brasil, há registros no norte do estado do Pará.Trata-se de espécie facilmente encontrada no solo de ambientes florestais, que ocorre em uma região bem preservada, não havendo ameaças diretas que possam colocá-la em risco de extinção.Por essas razões, Anomaloglossus baeobatrachus foi avaliada como Menos Preocupante (LC).

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.968
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0320.029

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 head0.384
Teacher spread0.340 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractno

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