Bibliographic record
Abstract
AbstractIn this paper we assess how RADARSAT-2's technical enhancements in terms of polarization, spatial resolution, look direction, and orbit control will impact the potential utility of its data products for 32 applications in the fields of agriculture, cartography, disaster management, forestry, geology, hydrology, oceans, and sea and land ice. Our assessment relies on bibliographic sources and, in particular, case studies drawn from ongoing applications development work at the Canada Centre for Remote Sensing and the Canadian Ice Service. The applications potential of RADARSAT-2 data compared with that of RADARSAT-1 data is anticipated to improve in a major, moderate, and minor fashion for 3, 18, and 10 of the identified applications, respectively. For one of the applications considered, the increase in potential of RADARSAT-2 vis-à-vis RADARSAT-1 cannot be assessed because this application relies completely on RADARSAT-2's new full polarimetric capability. Dans cet article, nous évaluons l'impact des améliorations techniques de RADARSAT-2 au plan de la polarisation, de la résolution spatiale, de la direction de visée et du contrôle de l'orbite sur l'utilisation potentielle de ses produits de données pour 32 applications dans les domaines de l'agriculture, de la cartographie, de la gestion des catastrophes, de la foresterie, de la géologie, de l'hydrologie, des océans, de la glace de mer et de la glace continentale. Notre évaluation repose sur des sources bibliographiques et, plus particulièrement, sur des études de cas tirés des travaux de développement des applications en cours au Centre canadien de télédétection et au Service canadien des glaces. Le potentiel d'application des données RADARSAT-2, comparativement à celui des données RADARSAT-1, devrait être amélioré de façon considérable, moyenne ou faible respectivement dans 3, 18 et 10 des applications identifiées. Dans le cas d'une des applications prises en considération, l'accroissement du potentiel de RADARSAT-2 par rapport à RADARSAT-1 ne peut être évalué étant donné que cette application repose entièrement sur les nouvelles données polarimétriques de RADARSAT-2. [Traduit par la Rédaction]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".