{"id":"W2026402852","doi":"10.1117/12.868218","title":"Evaluating TerraSAR-X for the identification of tillage occurrence over an agricultural area in Canada","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Tillage; Environmental science; Agriculture; Remote sensing; Sustainability; Agricultural engineering; Land management; Environmental resource management; Business; Agroforestry; Geography; Engineering; Agronomy; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001032242,0.0005565754,0.0002981628,0.001702169,0.0009867807,0.00108526,0.000761176,0.0002667939,0.0008100515],"category_scores_gemma":[0.002510613,0.0001993855,0.0003470208,0.00239839,0.0003786031,0.0004295775,0.0004926971,0.000266078,0.0002394984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01000283,"about_ca_system_score_gemma":0.01046251,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9809096,"about_ca_topic_score_gemma":0.9887611,"domain_scores_codex":[0.9992594,0.00007705213,0.00003887727,0.0001041756,0.0003727411,0.0001477787],"domain_scores_gemma":[0.9978055,0.000250281,0.0001574275,0.00004266691,0.001531377,0.000212745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008643192,0.0004620173,0.8832198,0.0001499584,0.00021178,0.000488339,0.0008655826,0.03044129,0.008754192,0.0004852293,0.001908517,0.0721489],"study_design_scores_gemma":[0.0000487895,0.0002675402,0.9084923,0.00003265644,0.00009700276,0.0001227259,0.003811095,0.08118717,0.003275991,0.00006439937,0.002556432,0.00004396349],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943599,0.0001521032,0.0008333357,0.00006313306,0.000004116718,0.0001149699,0.001352695,0.00009463026,0.003025011],"genre_scores_gemma":[0.9907883,0.0002559116,0.004350785,0.00002848565,0.000002322142,0.00002641987,0.002750866,0.00001510968,0.001781832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01909035,"threshold_uncertainty_score":0.07257587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01485295928780969,"score_gpt":0.2461233455745507,"score_spread":0.231270386286741,"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."}}