{"id":"W2103419569","doi":"10.1109/ccece.2004.1347709","title":"CMOS imager design for fast centroid readout","year":2004,"lang":"en","type":"article","venue":"","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Centroid; Chip; Pixel; Computer science; CMOS; Triangulation; Image sensor; Range (aeronautics); Computer hardware; Electronic engineering; Artificial intelligence; Engineering; Telecommunications; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004662168,0.0004353564,0.0004872119,0.0003116018,0.0002986857,0.000665954,0.001188265,0.0005777323,0.002742865],"category_scores_gemma":[0.001132993,0.00028025,0.0002700218,0.000324144,0.0001417071,0.0005319032,0.0002866548,0.0003484924,0.001642525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009236127,"about_ca_system_score_gemma":0.0007278898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008878773,"about_ca_topic_score_gemma":0.001398372,"domain_scores_codex":[0.9993199,0.00005376498,0.00004988387,0.0001409834,0.0003831113,0.00005233472],"domain_scores_gemma":[0.9993172,0.00008994586,0.0001283831,0.00007074435,0.0003679556,0.00002573681],"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":[0.0001649935,0.00008118009,0.0006306015,0.0005099043,0.0000379866,0.0002726135,0.0001240644,0.007006112,0.859464,0.009002643,0.00541052,0.1172954],"study_design_scores_gemma":[0.00009125096,0.0007542938,0.002291896,0.000037974,0.00007814619,0.002111152,0.00002982573,0.08269133,0.8292878,0.001142657,0.08140923,0.00007446743],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03962556,0.001080421,0.9431435,0.0002947392,0.0002357893,0.0006128919,0.0003779823,0.003417191,0.01121188],"genre_scores_gemma":[0.2328151,0.0007829893,0.7541783,0.0002720171,0.0001395622,0.0004134733,0.0003808797,0.0002417802,0.010776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002742865,"threshold_uncertainty_score":0.009175777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01147175506838036,"score_gpt":0.203569862533479,"score_spread":0.1920981074650986,"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."}}