{"id":"W2146633954","doi":"10.5539/mas.v4n2p49","title":"Sorting Raisins by Machine Vision System","year":2010,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sorting; Computer science; Machine vision; Artificial intelligence; Conveyor belt; Computer vision; Feature (linguistics); Image processing; Segmentation; Pattern recognition (psychology); Algorithm; Image (mathematics); Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006764489,0.0006468136,0.0008298742,0.001539854,0.0006024035,0.001501746,0.001295764,0.001386129,0.005565149],"category_scores_gemma":[0.000903606,0.0003464659,0.0004477275,0.001015582,0.0003451504,0.001113495,0.0004227964,0.0005765568,0.003107199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007306607,"about_ca_system_score_gemma":0.00101394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00195018,"about_ca_topic_score_gemma":0.001673274,"domain_scores_codex":[0.9990757,0.00007219552,0.00005459372,0.0002573485,0.0004638874,0.00007634432],"domain_scores_gemma":[0.9994825,0.00008029978,0.00004057986,0.00006439594,0.0003070486,0.00002505829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003525189,0.0001665177,0.001610496,0.0005265436,0.00007536352,0.000161981,0.0001675125,0.009830818,0.3179373,0.003839098,0.006363803,0.6589682],"study_design_scores_gemma":[0.0002149479,0.001436262,0.01196385,0.0002377786,0.0002699222,0.001435103,0.0002377269,0.4331105,0.4485801,0.004404696,0.09785912,0.0002499753],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03030339,0.0008393159,0.9509572,0.0001770414,0.0003108176,0.0003678894,0.0001966132,0.008890864,0.007956853],"genre_scores_gemma":[0.2405081,0.001159678,0.7404641,0.0004166751,0.0001610399,0.0004048169,0.0006916171,0.0001981343,0.0159959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005565149,"threshold_uncertainty_score":0.01861721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005379813127603373,"score_gpt":0.1983584537244826,"score_spread":0.1929786405968792,"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."}}