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Record W4390451743 · doi:10.37002/salve.ficha.34337.2

Ficha de Astyanax microschemos

2023· dataset· es· W4390451743 on OpenAlexaff
André Teixeira da Silva, Angela Maria Zanata, Augusto Luís Bentinho Silva, Bianca de Freitas Terra, Carla Simone Pavanelli, Dário Ernesto da Silva, Filipe Augusto Gonçalves de Melo, Frederico Fernandes Ferreira, Gabriel de Carvalho Deprá, Giancarlo Arrais Galvão, Gilberto Nepomuceno Salvador, Iago de Souza Penido, José Luís Olivan Birindelli, João Pedro Corrêa Gomes, Leonardo Oliveira Silva, Luciano de Freitas Barros Neto, Luisa Maria Sarmento Soares Filho, Luiz Fernando Caserta Tencatt, Luiz Fernando Duboc da Silva, Marcelo Fulgêncio Guedes de Brito, Priscila Camelier de Assis Cardoso, Roberto Esser dos Reis, Sergio Maia Queiroz Lima, Silvia Yasmin Lustosa Costa, Telton Pedro Anselmo Ramos, Thais de Assis Volpi, Tiago Casarim Pessali, Veronica de Barros Slobodian Motta, Érick Cristófore Guimarães.

Bibliographic record

VenueDatasets - Sistema SALVE - ICMBio · 2023
Typedataset
Languagees
FieldEnvironmental Science
TopicFish biology, ecology, and behavior
Canadian institutionsAtlantic School of Theology
Fundersnot available
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)0.005
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 score0.042
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0410.044

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 head0.292
Teacher spread0.267 · 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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