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Record W2181949739 · doi:10.25119/praxis-4-7-548

O uso de coleções zoológicas como ferramenta didática no ensino superior: um relato de caso

2012· article· pt· W2181949739 on OpenAlexaff
Hugo José Coelho Corrêa de Azevedo, Ronaldo Figueiró, Dimitri Ramos Alves, Valéria Vieira, André R. Senna

Bibliographic record

VenueRevista Práxis Online/Revista Práxis · 2012
Typearticle
Languagept
FieldSocial Sciences
TopicScience and Education Research
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Este artigo fala sobre a utilização de coleções zoológicas como recurso didático para o processo de ensino e aprendizagemem nível de graduação, nos cursos de Ciências Biológicas e Engenharia Ambiental, em uma Instituição deEnsino Superior (IES) da região sul Fluminense, Estado do Rio de Janeiro. Diferentes estratégias metodológicas sãonecessárias e utilizadas para o ensino da biologia e da zoologia, lançando-se mão da utilização de espaços formais e/ ou informais. Relata-se aqui a experiência de atualização e curadoria de uma coleção didática de zoologia, em moldessimilares aos de uma coleção científica. O objetivo desse projeto foi trazer aos estudantes uma maior compreensãosobre a diversidade biológica, assim como um melhor entendimento sobre as relações de parentesco entre as espécies, táxons supra-específicos, e o ser humano, além do conhecimento sobre a composição faunística dos mais diversos ecossistemas, sejam eles, terrestres, marinhos ou límnicos.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0020.004
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.101
GPT teacher head0.420
Teacher spread0.319 · 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 designCase report
Domainnot available
GenreEmpirical

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

Citations7
Published2012
Admission routes1
Has abstractyes

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