{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003045072,0.001238449,0.0004500176,0.001618786,0.0005406216,0.003681056,0.003111465,0.001822806,0.005713525],"category_scores_gemma":[0.003621398,0.0007441668,0.001462866,0.001041583,0.001370113,0.00310768,0.002661037,0.001970104,0.003208529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001140947,"about_ca_system_score_gemma":0.002573928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001211995,"about_ca_topic_score_gemma":0.001928783,"domain_scores_codex":[0.9983357,0.000314441,0.0001383303,0.0002079801,0.0008559733,0.0001477221],"domain_scores_gemma":[0.9982613,0.0005306494,0.0001927861,0.000449784,0.0004625033,0.000102907],"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":[0.00005148511,0.0002159926,0.001897977,0.002772556,0.0001228024,0.0004144968,0.0009503996,0.06381942,0.05679406,0.2158823,0.01053273,0.6465458],"study_design_scores_gemma":[0.0000587058,0.0004617472,0.001255389,0.001346378,0.0001138427,0.0008038761,0.0009202493,0.2010775,0.06724671,0.1600211,0.5665563,0.0001380464],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004035291,0.002020652,0.9718859,0.0009140272,0.0001481878,0.0001995887,0.00009634914,0.001353884,0.01934608],"genre_scores_gemma":[0.02955838,0.003719098,0.960497,0.0003713026,0.00005428955,0.0002233633,0.0003329492,0.0004186081,0.004824979],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005713525,"threshold_uncertainty_score":0.01911366,"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."}}