{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007582716,0.0002232477,0.0002200502,0.0001034813,0.0001041777,0.0000562221,0.0001458497,0.00008062657,0.00004805908],"category_scores_gemma":[0.00002686845,0.0002354515,0.0001099182,0.0002322675,0.00004489812,0.00007943581,0.000007341019,0.0001429757,0.0002687225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000544035,"about_ca_system_score_gemma":0.00000847807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009020126,"about_ca_topic_score_gemma":0.000002146037,"domain_scores_codex":[0.9989331,0.00001212916,0.0002417868,0.0002622124,0.000137065,0.0004137373],"domain_scores_gemma":[0.9993284,0.0000709276,0.00003065641,0.0003879259,0.00006785525,0.0001142127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001191922,0.000376731,0.0002338135,0.0004161156,0.0003333083,0.00007778127,0.002112143,0.2335183,0.4926107,0.02597983,0.1172473,0.1269749],"study_design_scores_gemma":[0.003969356,0.0002741755,0.00948752,0.0001043596,0.0002899287,0.0001525586,0.001191505,0.2488054,0.2703722,0.01955695,0.4428023,0.002993649],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9763321,0.0001206419,0.004317114,0.001326737,0.0004810766,0.0009707402,0.0001015705,0.001706477,0.01464359],"genre_scores_gemma":[0.9876075,0.00003376656,0.009058008,0.0002020867,0.0004493103,0.00002104754,0.00004502737,0.000065108,0.002518135],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3255551,"threshold_uncertainty_score":0.9601434,"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."}}