Análise do cenário de observação da Missão Sentinel-1
Bibliographic record
Abstract
A primeira constelação de satélites da série Sentinel da Agência Espacial Europeia (ESA), no Programa Copernicus, denominada Sentinel-1 pretende dar continuidade ao legado das missões ERS e ENVISAT da ESA e da missão RADARSAT da Agência Espacial do Canadá, com observações da superfície terrestre mediante imageamento por radar de abertura sintética. Esta missão apresenta uma quantidade considerável de aplicações para a comunidade científica e a sociedade em geral, como por exemplo, no controle e monitoramento ambiental. Este trabalho tem por objetivo analisar os fundamentos e desdobramentos desta missão, as aplicações dos dados SAR (Synthetic Aperture Radar) gerados e discutir as condições para a otimização do cenário da missão a nível global sobre o ambiente terrestre. ABSTRACT: The first Sentinel satellite constellation of the European Space Agency (ESA) in the Copernicus Program called Sentinel-1 aims to continue the legacy of the ESA's ERS and ENVISAT missions and RADARSAT mission of the Canadian Space Agency with observations of the terrestrial surface by Synthetic Aperture Radar (SAR) imaging. This mission presents a considerable amount of applications to the scientific community and society in general, such as environmental monitoring and control. The objective of this work is to analyze the fundamentals and developments of this mission, the applications of SAR data generated and to discuss the conditions for the optimization of the scenario of the global mission on the terrestrial environment.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".