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Record W4388179253 · doi:10.37002/salve.ficha.20447.2

Ficha de Furipterus horrens

2023· dataset· es· W4388179253 on OpenAlexaff
Enrico Bernard, Adriana Ruckert da Gama, Augusto Milagres E Gomes, Ciro Líbio Caldas dos Santos, Erich Arnold Fischer, Eugenia de Jesus Cordero Schmidt, Fernanda Atanaena Gonçalves de Andrade, Fábio de Carvalho Falcão, Guilherme Siniciato Terra Garbino, Juan Carlos Vargas Mena, Júlia Lins Luz, Leonardo Carreira Trevelin, Ludmilla Aguiar, Maria João Veloso da Costa Ramos Pereira, Mariana Delgado, Marlon Zortéa, Patrício Adriano da Rocha, Paulo Estefano Dineli Bobrowiec, Roberto Leonan Morim Novaes, Valéria da Cunha Tavares, William Douglas de Carvalho, Wilson Uieda

Bibliographic record

VenueDatasets - Sistema SALVE - ICMBio · 2023
Typedataset
Languagees
FieldBiochemistry, Genetics and Molecular Biology
TopicProtist diversity and phylogeny
Canadian institutionsEncana (Canada)
FundersInstituto Chico Mendes de Conservação da Biodiversidade
KeywordsComputer science

Abstract

fetched live from OpenAlex

frequentemente cavernas, que são ambientes altamente sensíveis a perturbações, com características físicas próprias e de pequena amplitude geográfica, configurando

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)0.000
metaresearch head score (Gemma)0.001
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 score0.025
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.015

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 head0.016
GPT teacher head0.273
Teacher spread0.257 · 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 abstractyes

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