DigiAtlas: Dispositivos Móveis Auxiliando o Ensino Multidisciplinar de Questões Ambientais
Bibliographic record
Abstract
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 troca de conhecimento entre os usuários.O aplicativo foi desenvolvido para dispositivos móveis e pode ser utilizado como ferramenta de pesquisa e de ensino-aprendizagem, em salas de aula ou atividades de campo, inclusive de maneira multidisciplinar. IntroduçãoA temática ambiental se mostra complexa pela sua essência, já que o ambiente e os seres dependentes deste estão interligados através de complexos processos e relações.Com isso, a abordagem multidisciplinar de questões ambientais acaba por facilitar seu entendimento.A multidisciplinaridade torna-se necessária, já que o ambiente, e as importantes questões atuais, como a degradação e fragmentação das áreas nativas, o efeito estufa e as mudanças climáticas, são assuntos complexos que exigem o entendimento de várias disciplinas para sua compreensão.Além disso, essa abordagem pode trazer o interesse dos alunos, permitindo um olhar multifocal e dinâmico à aprendizagem e ao ensino [Duso & Borges, 2010].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".