{"id":"W4389792491","doi":"10.3389/fmtec.2023.1282843","title":"Leveraging I4.0 smart methodologies for developing solutions for harvesting produce","year":2023,"lang":"en","type":"article","venue":"Frontiers in Manufacturing Technology","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"University of Windsor","keywords":"Computer science; Robotics; Obstacle; CAD; Manufacturing engineering; Key (lock); Artificial intelligence; Systems engineering; Robot; Engineering management; Engineering; Engineering drawing","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":[0.0008331375,0.0001860767,0.0002986436,0.0001570556,0.0005142233,0.00004634758,0.0004230718,0.0002435284,0.000002290341],"category_scores_gemma":[0.0007311285,0.00008637389,0.00009588191,0.000642696,0.00008032918,0.0001204083,0.0002153831,0.0001796748,0.000004831527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007921758,"about_ca_system_score_gemma":0.00001257715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003360986,"about_ca_topic_score_gemma":0.0001317261,"domain_scores_codex":[0.998303,0.00003943637,0.0002715641,0.0005044654,0.00008124853,0.0008003165],"domain_scores_gemma":[0.9993025,0.0004372936,0.00009727936,0.00008845934,0.00004805763,0.00002640065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007052228,0.00003813989,0.02849928,0.0001474126,0.0000968044,0.00001024669,0.0003468159,0.0004225496,0.07218267,0.002441112,0.0811657,0.8145788],"study_design_scores_gemma":[0.0006540468,0.0002475308,0.153001,0.0001753318,0.00004166501,0.00001850347,0.006964236,0.0009373119,0.3054216,0.2000992,0.3315055,0.0009340838],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9381543,0.0002404967,0.0365855,0.02122124,0.001359083,0.001055239,0.000023321,0.001306614,0.00005422685],"genre_scores_gemma":[0.6209502,0.00009417198,0.3759278,0.0002918543,0.0005056338,0.0008393803,0.0001959866,0.000007237818,0.00118773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8136446,"threshold_uncertainty_score":0.3955042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09485172799908467,"score_gpt":0.2784564921021832,"score_spread":0.1836047641030985,"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."}}