{"id":"W2120067191","doi":"10.3390/s90100430","title":"CMOS Image Sensors for High Speed Applications","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":195,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McMaster University","funders":"King Abdulaziz City for Science and Technology; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"CMOS; Image sensor; Pixel; Computer science; Rolling shutter; Frame rate; CMOS sensor; Electronic engineering; Chip; Computer hardware; Electrical engineering; Engineering; Artificial intelligence; Telecommunications; Shutter","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":[0.000306456,0.0004745927,0.0003877817,0.0005000693,0.0003405602,0.000913418,0.0006176485,0.0008163004,0.01265265],"category_scores_gemma":[0.000711531,0.0002680907,0.0002543637,0.0007534254,0.0002297245,0.0008935461,0.0004409393,0.000772561,0.006348127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005176202,"about_ca_system_score_gemma":0.0005722822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004116738,"about_ca_topic_score_gemma":0.0004937525,"domain_scores_codex":[0.9993976,0.00004860461,0.00002807442,0.00006989572,0.0004241183,0.00003165478],"domain_scores_gemma":[0.9997341,0.00003798958,0.00002814377,0.00002516697,0.0001568396,0.00001776316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001592224,0.00005541563,0.0005449488,0.001384128,0.00003680896,0.0001619603,0.0001476088,0.002025286,0.3680378,0.04341768,0.04701954,0.5370095],"study_design_scores_gemma":[0.00003670913,0.0002069817,0.0008780205,0.0001254617,0.00004356408,0.0007870116,0.00004628037,0.009247534,0.1644939,0.007929079,0.8161656,0.00003980298],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01985127,0.1439026,0.6623704,0.00420379,0.003337319,0.0005384968,0.001547103,0.006726337,0.1575227],"genre_scores_gemma":[0.2178812,0.08158843,0.5772157,0.002782352,0.001475111,0.0004812886,0.001662727,0.0003906029,0.1165225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01265265,"threshold_uncertainty_score":0.0423274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0063311035286572,"score_gpt":0.2232451986510173,"score_spread":0.2169140951223601,"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."}}