ANÁLISE CIENCIOMÉTRICA TEMPORAL COM REFERÊNCIA AOS MODELOS DIGITAIS DE ELEVAÇÃO – MDE: IMPORTÂNCIA E TENDÊNCIAS
Bibliographic record
Abstract
Com o aumento gradual das geotecnologias e o avanco cientifico relacionado as problematicas ambientais, estudos e trabalhos que estao se utilizando de modelos de elevacao, tem recebido acentuados estimulos. O objetivo deste trabalho foi realizar uma analise quantitativa temporal, por meio da tecnica da cienciometria, considerando o numero de citacoes que os termos: Digital Elevation Model – DEM e Digital Terrain Model DTM, receberam num periodo de 50 anos (1959 a 2009). Nessa pesquisa, foram inclusos 7314 artigos para DEM e 4.143 artigos para DTM, publicados em 6.758 revistas para e 3.525 revistas para o termo DTM. Desse modo, foi possivel constatar que pesquisadores de varias nacionalidades vem trabalhando os temas propostos, com predominio daqueles provindos de paises desenvolvidos (Alemanha, Canada, China, EUA e Italia). No Brasil, a producao dos temas, embora ainda seja pequena, e coincidente com o resultado obtido para outros temas analisados por meio da mesma tecnica de pesquisa. Com esse pressuposto, e necessario que haja um maior investimento na area, para que dessa forma a producao cientifica no pais venha a aumentar, principalmente ao que se refere ao objeto deste estudo.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".