{"id":"W4403061186","doi":"10.1109/iotaai62601.2024.10692829","title":"Inkjet printer ink drop feature detection based on machine vision","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Inkwell; Computer science; Drop (telecommunication); Computer vision; Artificial intelligence; Machine vision; Feature (linguistics); Drop out; Computer graphics (images); Speech recognition","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0003056859,0.0003924767,0.0005163845,0.001234564,0.0001737795,0.000611095,0.0007367911,0.0007190117,0.001203687],"category_scores_gemma":[0.000829764,0.000264329,0.000369071,0.0008131972,0.0003068144,0.0008200547,0.0003528753,0.0005697705,0.0005523752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003663279,"about_ca_system_score_gemma":0.0003127994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008235454,"about_ca_topic_score_gemma":0.0008179153,"domain_scores_codex":[0.999279,0.00005108837,0.00002186545,0.0001450164,0.000459917,0.00004314262],"domain_scores_gemma":[0.9994445,0.0001603735,0.00005995805,0.00005184678,0.0002609352,0.00002235931],"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":[0.0001519236,0.0001107253,0.0012578,0.0001899605,0.00003792545,0.0001317841,0.00006229624,0.004660377,0.6717638,0.0008217864,0.001087507,0.319724],"study_design_scores_gemma":[0.00004237587,0.0003966928,0.01036162,0.00002535819,0.00004944362,0.0007284037,0.00003372945,0.4687825,0.515574,0.000743737,0.003182587,0.00007957328],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1250109,0.0007160286,0.8670614,0.00009707338,0.0001155512,0.0001261077,0.0001360023,0.002928043,0.003808797],"genre_scores_gemma":[0.5907102,0.0005439803,0.4053138,0.0001071229,0.00005020372,0.0001165899,0.000201284,0.0000844502,0.00287242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001234564,"threshold_uncertainty_score":0.004026711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007294618276654879,"score_gpt":0.2303849456280756,"score_spread":0.2230903273514207,"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."}}