{"id":"W2096091194","doi":"10.1109/jssc.2009.2016693","title":"Focal-Plane Algorithmically-Multiplying CMOS Computational Image Sensor","year":2009,"lang":"en","type":"article","venue":"IEEE Journal of Solid-State Circuits","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Pixel; CMOS; Quantization (signal processing); Computer science; Bit plane; Kernel (algebra); Binary number; Artificial intelligence; Computer vision; Mathematics; Electronic engineering; Engineering; Arithmetic","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":[0.0001230307,0.0002147153,0.0002061438,0.000195102,0.0002569841,0.0003632994,0.0007488753,0.0001913629,0.003202369],"category_scores_gemma":[0.0004086346,0.0001186229,0.0001150597,0.0002975485,0.0002020495,0.00030256,0.000240211,0.0002166033,0.0007249691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000631799,"about_ca_system_score_gemma":0.00070165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001391436,"about_ca_topic_score_gemma":0.003091189,"domain_scores_codex":[0.9997923,0.0000106549,0.000008888041,0.00004634139,0.0001235363,0.00001823099],"domain_scores_gemma":[0.9998409,0.00002991597,0.0000182902,0.0000217819,0.00007644004,0.0000128121],"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":[0.0002599379,0.00007975167,0.001351056,0.0002083124,0.00001618056,0.000162093,0.00009285031,0.008807099,0.7976173,0.02444365,0.007008997,0.1599528],"study_design_scores_gemma":[0.00005003124,0.000389379,0.003825752,0.00001438999,0.00004166742,0.0009852552,0.00004897744,0.1699166,0.7814119,0.002990231,0.04027849,0.00004740804],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2059672,0.0004537929,0.76355,0.0004020205,0.0001401958,0.0002689234,0.0008428589,0.003284448,0.02509054],"genre_scores_gemma":[0.4475777,0.0002192812,0.5415338,0.0001672986,0.00003615479,0.0001227256,0.0004377163,0.00005174788,0.009853564],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003202369,"threshold_uncertainty_score":0.01071304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01126715457950456,"score_gpt":0.2471776498252617,"score_spread":0.2359104952457572,"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."}}