{"id":"W4402299672","doi":"10.1109/tafe.2024.3444730","title":"Adaptive Feature-Based Plant Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on AgriFood Electronics","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Feature (linguistics); Computer science; Pattern recognition (psychology); Artificial intelligence; Linguistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001005523,0.000213789,0.0001459462,0.00003004828,0.0002388184,0.0001169375,0.0001341035,0.0001844253,0.0002917373],"category_scores_gemma":[0.000001517282,0.00007904631,0.0002103486,0.0006021057,0.00002372285,0.0001303927,5.405791e-7,0.0005313579,0.0003206356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001015278,"about_ca_system_score_gemma":0.00003722679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002791571,"about_ca_topic_score_gemma":0.001228719,"domain_scores_codex":[0.9987991,0.00005206544,0.0001400215,0.0003805575,0.0002300358,0.0003981543],"domain_scores_gemma":[0.9995856,0.0001974338,0.00002633501,0.00004966216,0.00004520246,0.00009571863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003395119,0.0005576934,0.00000417958,0.00002573142,0.0002105827,0.00003819433,0.00008589691,0.0008151214,0.251229,0.0004136476,0.01898175,0.7272987],"study_design_scores_gemma":[0.0005173098,0.005267469,0.0006784619,0.0003171171,0.0002901268,0.0001136616,0.0002060558,0.00616495,0.6685097,0.00193708,0.3148092,0.00118887],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9060024,0.006707517,0.04527854,0.02184825,0.004770198,0.002101371,0.002937202,0.003604562,0.006749941],"genre_scores_gemma":[0.9981112,0.000180901,0.0000963784,0.0005211122,0.0003139741,0.00006424629,0.0001637041,0.000002668663,0.0005457705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7261099,"threshold_uncertainty_score":0.4121229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0193057117888795,"score_gpt":0.1996553401373323,"score_spread":0.1803496283484528,"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."}}