{"id":"W2767307300","doi":"10.1080/01431161.2017.1395973","title":"On the use of temporal vegetation indices in support of eligibility controls for EU aids in agriculture","year":2017,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Università degli Studi della Basilicata; Regione Basilicata; Concordia University of Edmonton","keywords":"Arable land; Thematic Mapper; Context (archaeology); Orthophoto; Remote sensing; Vegetation (pathology); Identification (biology); Agricultural land; Land use; Satellite imagery; Common Agricultural Policy; Computer science; Agriculture; Cartography; Geography; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002794745,0.0002585314,0.0002102256,0.001656892,0.00021371,0.001121487,0.0004520053,0.0003639587,0.0008832494],"category_scores_gemma":[0.005873213,0.00007773347,0.0002112362,0.0007844802,0.0001828247,0.0003867086,0.0003771378,0.0002173114,0.0002218538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003208763,"about_ca_system_score_gemma":0.0006458908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006553887,"about_ca_topic_score_gemma":0.01058485,"domain_scores_codex":[0.9990405,0.0005755284,0.00005952892,0.0001132303,0.0001447836,0.00006625811],"domain_scores_gemma":[0.9968628,0.001931171,0.0004541756,0.0001741807,0.0004961023,0.0000816177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007450988,0.0004231231,0.3448946,0.0002034266,0.0001698738,0.0002006065,0.0004573521,0.05677857,0.008376963,0.005130085,0.003294326,0.5793259],"study_design_scores_gemma":[0.0000579746,0.000472371,0.448451,0.0001844975,0.0001371482,0.000196754,0.001108476,0.5291284,0.00884357,0.002899504,0.008463393,0.00005701512],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9163416,0.0008973447,0.06864398,0.0005295382,0.00005845967,0.0002024622,0.001070431,0.0002548446,0.01200141],"genre_scores_gemma":[0.9644014,0.0001743144,0.03416587,0.0000323323,0.00001689805,0.00004023068,0.0004258334,0.00001248563,0.0007306989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006553887,"threshold_uncertainty_score":0.01478016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03029349347581983,"score_gpt":0.2996066246372549,"score_spread":0.2693131311614351,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}