Special Issue on Applied Earth Observation and Remote Sensing in Latin America
Bibliographic record
Abstract
This special Issue focused on recent research led by South American researchers and teams. It is a long overdue pos- sibility offered to researchers in this geographical area to share their excellent work with the international community. Accord- ingly, the response to the call for papers was overwhelming, with more than 60 papers submitted from eight countries. Eventually, 23 articles were accepted, among which 11 are authored from Brazil, while Argentina and Mexico contribute each with five papers, and Colombia and Ecuador have one arti- cle accepted each. Testifying the international breadth of these researches, seven of these contributions have coauthors from outside Latin America: two from Italy, and one from France, Canada, Finland, USA, and Germany. Before describing the contributions that have been selected for this issue, it is worth recalling briefly the history and current situation of remote sensing activities in the three major countries in the area, which, as mentioned, contribute to the large majority of the works published in the following pages.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.089 | 0.024 |
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".