{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005252761,0.000142752,0.0001369432,0.00001227064,0.0007539596,0.0002223159,0.000625137,0.00007716454,0.00006871755],"category_scores_gemma":[0.00001860112,0.00004595092,0.00004403609,0.0005909675,0.0002130194,0.0001883326,0.000152456,0.0002205429,0.0001041092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002098596,"about_ca_system_score_gemma":0.00001067391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007645966,"about_ca_topic_score_gemma":0.0002409412,"domain_scores_codex":[0.9984069,0.000008538223,0.0001878841,0.0004960967,0.00048475,0.0004157966],"domain_scores_gemma":[0.9995355,0.00004936771,0.00008986984,0.00009474419,0.00005327509,0.0001772185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003424738,0.00002832914,0.0004328302,0.0000020418,7.421675e-7,6.515711e-7,0.00006495581,0.000001351999,0.9548951,0.001188576,0.0006404397,0.04274153],"study_design_scores_gemma":[0.000627991,0.0002748162,0.08969941,0.00005374181,0.00002815585,0.0000761249,0.001161686,0.04969803,0.7746145,0.002617095,0.07959089,0.001557521],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765814,0.00002966658,0.0003606954,0.0005138313,0.0002621709,0.0001803485,0.00001462909,0.0001973723,0.02185988],"genre_scores_gemma":[0.9990848,0.000001164971,0.0002049568,0.0002209388,0.0002209319,0.00001412185,0.00001877432,8.109673e-7,0.0002335515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1802806,"threshold_uncertainty_score":0.5798924,"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."}}