{"id":"W2895076274","doi":"10.1007/978-3-030-01267-0_32","title":"Revisiting Autofocus for Smartphone Cameras","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Autofocus; Computer science; Computer vision; Artificial intelligence; Computer graphics (images); Focus (optics)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.000277589,0.0002392887,0.0002331059,0.00020548,0.0001582195,0.000166577,0.000562696,0.0001573164,0.00003133204],"category_scores_gemma":[0.0000306751,0.0002345527,0.00005936885,0.0001673511,0.0002703562,0.0001032106,0.0001232583,0.0002817694,0.00002094731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001334959,"about_ca_system_score_gemma":0.00007539407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002818455,"about_ca_topic_score_gemma":0.000004180067,"domain_scores_codex":[0.9988237,0.000002040012,0.0002402337,0.0004382123,0.0001789505,0.0003168568],"domain_scores_gemma":[0.9992278,0.0001119165,0.000065277,0.0004096189,0.0001344556,0.0000509511],"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":[0.000001096917,0.000002502711,0.000002167906,0.0001133148,0.000004666572,0.000002434725,0.0001639712,0.006000526,0.000771317,0.0008873709,0.0003124587,0.9917382],"study_design_scores_gemma":[0.000118594,0.00004445959,0.000010488,0.0007796308,0.00001347604,0.00002158769,7.878959e-8,0.8176739,0.01506171,0.1328363,0.03283501,0.0006048227],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004310741,0.0005354725,0.9949352,0.000170353,0.000323484,0.0002809968,0.000009853493,0.0004412226,0.003260267],"genre_scores_gemma":[0.03538111,0.00004563663,0.9623067,0.0003540691,0.001380308,0.00004823362,0.00001084803,0.00007359044,0.0003995223],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9911333,"threshold_uncertainty_score":0.9564782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0140887888097652,"score_gpt":0.2528627446485767,"score_spread":0.2387739558388115,"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."}}