Évaluation de quelques sources d’erreur dans un inventaire semi-automatisé de l’utilisation agricole du sol : étude de cas à Sainte-Foy
Bibliographic record
Abstract
Un inventaire de l'occupation du sol pour chaque terrain agricole peut être produit par le croisement (automatisé) entre une image satellitaire classifiée et une image des terrains, provenant du cadastre numérisé. Trois sources d'erreur sont examinées. L'erreur planimétrique, créée par la « rasteurisation » des polygones-terrain, Terreur dans les proportions des classes d'occupation du sol due à la résolution spatiale, et l'erreur de la classification elle-même. Les résultats démontrent que chacune de ces erreurs varie en fonction des terrains, donnant des inventaires de qualité inégale. Il est difficile de le corréler avec les dimensions des terrains, et de fixer ainsi des dimensions minimales nécessaires au succès de l'inventaire agricole dans un milieu péri-urbain.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".