{"id":"W2106556101","doi":"10.1109/icsmc.2009.5345994","title":"Ripe tomato extraction for a harvesting robotic system","year":2009,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ontario Centres of Excellence","keywords":"Greenhouse; Cluster analysis; Artificial intelligence; Computer vision; Computer science; Feature extraction; Extraction (chemistry); Horticulture; Chemistry; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009084625,0.00007824456,0.0000978688,0.000003615052,0.0001697546,0.00007170156,0.00007783689,0.00005380087,0.00004370378],"category_scores_gemma":[0.00002104912,0.00002141466,0.00007041841,0.0001362113,0.000004199896,0.0001411934,0.000005415927,0.00003457756,0.00003727898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001632048,"about_ca_system_score_gemma":0.000001536262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006505779,"about_ca_topic_score_gemma":0.0001533574,"domain_scores_codex":[0.9994575,0.00001292113,0.0001233696,0.0001566272,0.00007857983,0.000171004],"domain_scores_gemma":[0.9997488,0.00008862985,0.00004319376,0.00002102362,0.0000437426,0.00005457286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001919977,0.00009685707,0.001136465,0.0000159094,0.000008629167,0.00000408962,0.00003293871,0.00005466302,0.8166988,0.008744287,0.01313632,0.1600518],"study_design_scores_gemma":[0.0004991516,0.001434349,0.826977,0.0001829346,0.00007471895,0.0002157485,0.002876619,0.004722039,0.04134177,0.001007061,0.1198305,0.0008381169],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9790732,0.00006717852,0.0004844141,0.003120566,0.0002593921,0.0003742378,0.000003223824,0.0004164808,0.01620135],"genre_scores_gemma":[0.9956468,8.803479e-7,0.0007406232,0.0002772227,0.0005566979,0.00001254645,0.00002215925,2.220006e-7,0.002742852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8258405,"threshold_uncertainty_score":0.1305632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01989567581885991,"score_gpt":0.2271803545966239,"score_spread":0.207284678777764,"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."}}