{"id":"W4390503522","doi":"10.2139/ssrn.4681504","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 vision; Computer science; Robot; Embedded system; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0002844284,0.0002532946,0.0002979266,0.0001346063,0.00005936153,0.00007448586,0.0002144119,0.0003941214,0.000001043973],"category_scores_gemma":[0.00002255797,0.0002537658,0.00007882594,0.00018959,0.00004839668,0.00005202281,0.0003131329,0.006632044,0.000009025097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001886049,"about_ca_system_score_gemma":0.00006935524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002156843,"about_ca_topic_score_gemma":0.0007544143,"domain_scores_codex":[0.9980196,0.00002470891,0.0004560889,0.0003140417,0.0001536027,0.001031966],"domain_scores_gemma":[0.9995351,0.0000683502,0.00008821379,0.0002430631,0.00003254979,0.00003266315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001531937,0.00004471058,0.00003797326,0.000615546,0.0002004498,0.000006374843,0.0001821551,0.5793487,0.1339309,0.02806022,0.000008114689,0.2575495],"study_design_scores_gemma":[0.0001581124,0.00005295772,0.00005091802,0.0002054467,0.00003202727,0.000147385,0.0002231427,0.02154282,0.03613161,0.9408165,0.0002860332,0.0003531161],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2345026,0.009334159,0.7548396,0.0001887146,0.0002737867,0.0002264057,0.000002996712,0.0005503118,0.00008142036],"genre_scores_gemma":[0.9807031,0.008873281,0.01001653,0.000009009189,0.0002874068,0.00002882369,0.000003850264,0.00005404132,0.00002401743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9127562,"threshold_uncertainty_score":0.9999915,"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."}}