{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000124831,0.0003811363,0.0003774697,0.0002781026,0.0004442995,0.0004150977,0.0005291997,0.0005990338,0.004236825],"category_scores_gemma":[0.0001988016,0.0002968558,0.0002907906,0.0001781824,0.000186049,0.0005178245,0.0003671437,0.0003127303,0.001535874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002921021,"about_ca_system_score_gemma":0.0004786904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009626573,"about_ca_topic_score_gemma":0.001994692,"domain_scores_codex":[0.9998797,0.000006934043,0.000005145042,0.00004602374,0.00004964545,0.00001251685],"domain_scores_gemma":[0.999896,0.00001847782,0.00001351788,0.00001841897,0.00003656495,0.00001694738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001275505,0.00003261369,0.0007768422,0.0001112005,0.00001580638,0.0002716037,0.0000764479,0.004102458,0.872089,0.0006637182,0.001608133,0.1201247],"study_design_scores_gemma":[0.00009446782,0.0009349764,0.02132732,0.0000591684,0.0001023841,0.001756296,0.0002459897,0.2907033,0.6335567,0.001560537,0.04948928,0.0001695464],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1365407,0.0002784039,0.8487635,0.0003624563,0.0001077367,0.0002165362,0.0001799366,0.005822354,0.00772845],"genre_scores_gemma":[0.5104994,0.0002456133,0.4715747,0.0002763082,0.00004241489,0.0001751593,0.0003871233,0.0001783941,0.01662081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004236825,"threshold_uncertainty_score":0.01417357,"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."}}