ESTIMATION DES CONCENTRATIONS DE SEDIMENTS EN SUSPENSION DANS LES EAUX COTIERES A PARTIR D'IMAGES PLEIADES
Bibliographic record
Abstract
Les travaux de dragage peuvent occasionner la remise en suspension de sédiments contaminés dans la colonne d'eau et leur transport vers les sites aquacoles. Effigis, en collaboration avec Travaux publics et Services gouvernementaux Canada et l'Agence spatiale canadienne, a développé une méthodologie de suivi des concentrations de sédiments en suspension (CSS) en mer à partir d'imagerie satellitaire. Un examen de l'état de l'art a permis de passer en revue les méthodes existantes pour l'estimation des CSS, les capteurs qui répondent aux besoins et les protocoles d'échantillonnage terrain permettant de valider l'approche. La méthodologie retenue repose sur l'utilisation d'un modèle empirique pour l'estimation de la CSS à partir des réflectances de l'eau. La constellation Pléiades a été retenue en raison de son potentiel à estimer la CSS, sa capacité à acquérir des images à des dates spécifiques et à mettre en place une application opérationnelle. Les résultats montrent que les corrélations entre les mesures et les estimations de CSS s'approchent des 80%, avec des erreurs RMSE de l'ordre de 25%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".