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Record W2228769926 · doi:10.5753/cbie.wcbie.2015.693

DigiAtlas: Dispositivos Móveis Auxiliando o Ensino Multidisciplinar de Questões Ambientais

2015· article· pt· W2228769926 on OpenAlexfundno aff
Lucas Moreira Ferreira, Mariana Raniero, Gabriel Gerber Hornink, Paulo Alexandre Bressan

Bibliographic record

VenueAnais ... Workshops do Congresso Brasileiro de Informática na Educação · 2015
Typearticle
Languagept
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
FundersCanadian Bureau for International Education
KeywordsMultidisciplinary approachTheme (computing)Computer scienceWork (physics)GeographyEngineeringWorld Wide WebSociologySocial science

Abstract

fetched live from OpenAlex

Currently the environmental area is receiving greater attention.It is important for the understanding of our existence as part of a natural system and our responsibility under the same.This work presents the DigiAtlas application based on this theme, it features on a digital map, environment, abiotic and biotic data, and allows the management of these data.New data collected in the application can be shared, allowing the exchange of knowledge between users.The application was developed for mobile devices and can be used as a research tool, and teaching and learning in classrooms or field activities, including multidisciplinary way.Resumo.Atualmente a temática ambiental está recebendo grande atenção devido a sua importância para o entendimento de nossa existência, como parte de um sistema natural e de nossa responsabilidade sob o mesmo.Este artigo apresenta o aplicativo DigiAtlas com base nessa temática, ele apresenta em um mapa digital dados ambientais, abióticos e bióticos, além de permitir o gerenciamento desses dados.Novos dados coletados na aplicação podem ser compartilhados permitindo a

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0040.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.032
GPT teacher head0.316
Teacher spread0.284 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations1
Published2015
Admission routes1
Has abstractyes

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