Transforming environmental monitoring into action plans: Lessons learned from a decade of RADARSAT-1 initiatives in Latin America
Bibliographic record
Abstract
Over the past decade nations around the world have invested scarce resources in civilian radar remote sensing from space, through capital investments in satellites and data-acquisition programs, organizational support for training, technical assistance, and purchases of high-technology equipment and materials. This paper provides a retrospective on major collaborative initiatives that have taken place in Latin American related to RADARSAT-1 over this period and outlines challenges still to be faced if RADARSAT and other imaging radar systems are to reach their full potential to inform operational resource managers and policy-makers. The purpose of this paper is to assess RADARSAT-1 initiatives in Latin America in terms of accomplishments and lessons learned in three areas: (1) technology development, transfer, and exchange between Canadian and Latin American experts; (2) applications to natural resource monitoring and management through the involvement of operational users; and (3) incorporation into international and national policies for sustainable development. RADARSAT-1 achievements are measured in terms of long-term goals described in the EverGreen Plan, a 1989 proposal designed to build indigenous capabilities and encourage operational use and acceptance by political, economic, and business leaders. This assessment is based on the results of a survey of GlobeSAR-2 national coordinators and is set in the context of a review of RADARSAT-1 programs that have taken place in Latin America over the past decade. A thematic analysis of 278 papers presented at the IX Latin American Remote Sensing Symposium is included to highlight resource application areas in which RADARSAT-1 has proven most successful. The authors have played active roles in RADARSAT-1 initiatives during the past 10 years. Christine Nielsen was co-author of The EverGreen Plan, and served as a consultant to Radarsat International (RSI) in 1991, responsible for designing an international education strategy prior to the launch of RADARSAT-1. Tania Sausen is a leader in remote sensing training and education programs at INPE. Over the past ten years she has organized many seminars and training courses, primarily related to ERS. Sausen is President of Technical Commission VI ‐ Education and Communication for the International Society of Photogrammetry and Remote Sensing.
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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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".