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Record W4377000354 · doi:10.37002/salve.ficha.35863

Ficha de Amphisbaena longinqua

2023· dataset· es· W4377000354 on OpenAlexaff
Guarino Rinaldi Colli, Carlos Eduardo Guidorizzi, Yeda Soares de Lucena Bataus, Paula Ribeiro Anunciação, Luciana Signorelli, Carlos Roberto Abrahão, Róbson Waldemar Ávila, Diva Maria Borges‐Nojosa, Beatriz Nunes Cosendey, Henrique Caldeira Costa, Annelise Batista D ́Angiolella, Eduardo José dos Reis Dias, Iuri Ribeiro Dias, Renato Gomes Faria, Eliza María Xavier Freire, Luciana Frazão, Thaís Guedes, Mara Cíntia Kiefer, Renata Perez Maciel, Daniel Oliveira Mesquita, Leandro João Carneiro de Lima Moraes, Geraldo Jorge Barbosa de Moura, Tamí Mott, Cristiano de Campos Nogueira, Davi Lima Pantoja, Daniel Cunha Passos, Hugo Bonfim de Arruda Pinto, Leonardo Barros Ribeiro, Síria Lisandra de Barcelos Ribeiro, Marco Antônio Ribeiro‐Júnior, Diego José Santana, Marcélia Basto da Silva, Selma Torquato da Silva, Adriano Lima Silveira, Marcelo José Sturaró, Moacir Santos Tinôco, Lucas Rafael Uchôa, Fernanda P. Werneck, Gisele Regina Winck

Bibliographic record

VenueDatasets - Sistema SALVE - ICMBio · 2023
Typedataset
Languagees
FieldEarth and Planetary Sciences
TopicScarabaeidae Beetle Taxonomy and Biogeography
Canadian institutionsImpact
FundersInstituto Chico Mendes de Conservação da Biodiversidade
KeywordsGeography

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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 categoriesnone
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 score0.053
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0430.036

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 head0.025
GPT teacher head0.254
Teacher spread0.229 · 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
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
Published2023
Admission routes1
Has abstractno

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Same venueDatasets - Sistema SALVE - ICMBioSame topicScarabaeidae Beetle Taxonomy and BiogeographyFrench-language works237,207