{"id":"W2101234418","doi":"10.1109/ted.2005.856192","title":"Reset and Partition Noise in Active Pixel Image Sensors","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Electron Devices","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reset (finance); Transistor; Noise (video); Partition (number theory); Pixel; Electronic engineering; Computer science; Noise measurement; Flicker noise; Noise generator; Noise floor; Electrical engineering; Engineering; Artificial intelligence; Mathematics; CMOS; Noise figure; Noise reduction; Voltage; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004237948,0.0005522691,0.0004187484,0.0004767508,0.0002462387,0.0006588955,0.0008764405,0.0007852018,0.0004485966],"category_scores_gemma":[0.001777449,0.0004089764,0.0003518785,0.0003883409,0.0005620546,0.001086749,0.0003861873,0.0004854949,0.0001859339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006366431,"about_ca_system_score_gemma":0.0002287949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001027025,"about_ca_topic_score_gemma":0.001079737,"domain_scores_codex":[0.9994204,0.00009950549,0.00001548126,0.0001218337,0.0003070652,0.00003576838],"domain_scores_gemma":[0.9995527,0.0002512851,0.00005893014,0.00005618602,0.00006427687,0.0000166721],"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":[0.0009184394,0.000135296,0.01084696,0.000297186,0.00009971329,0.0008386453,0.0006155999,0.3489769,0.549097,0.02089928,0.0005856631,0.06668942],"study_design_scores_gemma":[0.00001329897,0.0002519278,0.00362614,0.00002355419,0.00003582875,0.0004350619,0.00004407668,0.8768907,0.1152002,0.002626808,0.0008201773,0.00003222628],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3940265,0.00164685,0.6005407,0.0001075772,0.00005113148,0.0000389685,0.0001139778,0.001159604,0.002314641],"genre_scores_gemma":[0.9872865,0.0002048219,0.0116222,0.00002400587,0.00001884359,0.00001588153,0.00005033916,0.00005244928,0.0007250389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001027025,"threshold_uncertainty_score":0.004619122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005939933650472447,"score_gpt":0.2274032282264604,"score_spread":0.221463294575988,"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."}}