{"id":"W2075271931","doi":"10.1109/argo-geoinformatics.2013.6621920","title":"AAFC annual crop inventory","year":2013,"lang":"en","type":"article","venue":"","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Growing season; Agriculture; Service (business); Crop; Environmental science; Agricultural engineering; Business; Computer science; Environmental resource management; Agricultural science; Agricultural economics; Geography; Forestry; Marketing; Engineering; Agronomy; Economics","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":[0.001032163,0.0009619142,0.0004687028,0.007265184,0.001149064,0.002008488,0.001643675,0.0004471818,0.08363773],"category_scores_gemma":[0.002526178,0.0003355151,0.0004386128,0.007525919,0.0001846074,0.001204783,0.000670494,0.0008211175,0.04716677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005924427,"about_ca_system_score_gemma":0.009969474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3119644,"about_ca_topic_score_gemma":0.2828143,"domain_scores_codex":[0.998893,0.00007212369,0.00006572664,0.0001306554,0.0007076641,0.0001308536],"domain_scores_gemma":[0.9969228,0.0001294275,0.0001442245,0.0001958245,0.002452902,0.0001547211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000729733,0.00003134983,0.002159243,0.0003550848,0.00002188228,0.00005562959,0.00005278936,0.0009474221,0.0005524515,0.002312823,0.8944101,0.09902834],"study_design_scores_gemma":[0.000005890388,0.000008034133,0.005383764,0.0000860211,0.000007079165,0.00004599752,0.00004401895,0.0004109882,0.0002799842,0.0003352086,0.9933808,0.00001216235],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005744131,0.003921657,0.003489119,0.001223488,0.0008610738,0.0004810327,0.7376666,0.002055765,0.2445571],"genre_scores_gemma":[0.03297642,0.008383484,0.01231802,0.0006762384,0.0002853811,0.0006819238,0.7417029,0.0006940393,0.2022816],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3119644,"threshold_uncertainty_score":0.6202971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004442797597243673,"score_gpt":0.1801404752278924,"score_spread":0.1756976776306488,"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."}}