{"id":"W4390507240","doi":"10.2139/ssrn.4681591","title":"Enhancing Yam Quality Detection Through Computer Vision in Iot and Robotics Applications","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Conestoga College","funders":"","keywords":"Robotics; Artificial intelligence; Internet of Things; Quality (philosophy); Computer science; Computer vision; Embedded system; Robot","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003046728,0.0005466556,0.0004068922,0.0008671081,0.0002209845,0.001063207,0.0003226911,0.0009190342,0.003122256],"category_scores_gemma":[0.0009257756,0.0003020859,0.0003616747,0.0009208886,0.0002602765,0.001159794,0.0005721851,0.0004725539,0.001049994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001625071,"about_ca_system_score_gemma":0.0002047151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007922197,"about_ca_topic_score_gemma":0.001408583,"domain_scores_codex":[0.999801,0.00003231115,0.000009468402,0.0000470516,0.00007861893,0.00003155429],"domain_scores_gemma":[0.9996643,0.0001032203,0.00004846287,0.00003845751,0.0001272061,0.00001837134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003729575,0.0001783847,0.004463332,0.0004353282,0.00005291942,0.0002141715,0.00009190095,0.01302414,0.460438,0.002785001,0.002752528,0.5151914],"study_design_scores_gemma":[0.00004892614,0.000383019,0.02595341,0.0001011405,0.0001550059,0.000951989,0.0001647815,0.6545382,0.297156,0.008002634,0.01246568,0.00007914369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09810518,0.001679664,0.889495,0.0002885494,0.0002879614,0.00005976373,0.0002484292,0.001479042,0.008356341],"genre_scores_gemma":[0.7114889,0.00177237,0.2787689,0.0001903077,0.0001830993,0.00003635458,0.0003788187,0.0002147617,0.006966456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003122256,"threshold_uncertainty_score":0.01044506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008708206108409463,"score_gpt":0.2771607735026621,"score_spread":0.2684525673942526,"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."}}