Adaptation aux paysages agricoles européens d’une méthode cartographique d’analyse de la configuration et de la composition des cultures
Bibliographic record
Abstract
Les paysages agricoles occupent pres de 40% de la superficie des terres disponibles. A ce titre, ils jouent un role important dans la conservation de la biodiversite. En retour, cette derniere contribue a la production agricole en fournissant des services ecosystemiques tels que la pollinisation ou la lutte biologique. Le role de l'heterogeneite du paysage agricole dans le maintien de la biodiversite se revele etre une piste de recherche interessante qui pourrait suggerer de nouvelles politiques agricoles visant a favoriser la biodiversite. Le projet FARMLAND (BiodivERsa2011-66) s’insere dans ce contexte en s'inspirant de travaux recents combinant des outils de geomatique, de teledetection et de geostatistique. Sur la base du calcul d'indices spatiaux, ces travaux proposent une methode pour realiser la selection de quadrats permettant de considerer l'influence sur la biodiversite, de l'heterogeneite de composition du paysage (nombre et proportions des differents types de couvert) et de l'heterogeneite de configuration (arrangement spatial des types de couvert). Ce projet doit aboutir a la selection d'une cinquantaine de carres dans 7 sites europeens au sein desquels seront effectues des releves de biodiversite et des evaluations des services ecosystemiques. Nous presentons les differentes etapes de la methode cartographique (classification et segmentation d'image satellitaire, analyse par fenetre mobile, requete multicritere) menant a la realisation du plan d'echantillonnage pour le site des Vallees et Coteaux de Gascogne qui sert de zone test pour les autres sites europeens. Nous discutons des adaptations necessaires et des differences avec le site de reference canadien.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".