{"id":"W2212854264","doi":"10.1109/led.2015.2410304","title":"Direct-Conversion CMOS X-Ray Imager With <inline-formula> <tex-math notation=\"LaTeX\">$5.6 ~\\mu \\text{m} \\times 6.25~\\mu \\text{m}$ </tex-math></inline-formula> Pixels","year":2015,"lang":"en","type":"article","venue":"IEEE Electron Device Letters","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Canadian Institutes of Health Research; CMC Microsystems","keywords":"Pixel; CMOS; Dot pitch; Physics; Noise (video); Image sensor; Optoelectronics; Materials science; Electrical engineering; Optics; Computer science; Engineering; Image (mathematics); Artificial intelligence","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":[0.0001918487,0.0004240644,0.0004242124,0.0002704374,0.0001950105,0.0006400693,0.000961812,0.0003194828,0.01414419],"category_scores_gemma":[0.0004294192,0.0003286331,0.0002197027,0.0003841893,0.0002403606,0.0005105442,0.0003694082,0.0004756541,0.00535006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004497562,"about_ca_system_score_gemma":0.0003607482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005244139,"about_ca_topic_score_gemma":0.00128347,"domain_scores_codex":[0.9997651,0.00001189006,0.00001441212,0.00007825423,0.0001131043,0.00001721048],"domain_scores_gemma":[0.9997831,0.00005423143,0.00003292585,0.00004969973,0.00006580284,0.00001435112],"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":[0.0001254125,0.00006110799,0.0005076769,0.0002526041,0.00002020257,0.0001958318,0.00007388864,0.000901642,0.9466903,0.004153091,0.008001059,0.03901721],"study_design_scores_gemma":[0.00003105487,0.0001291087,0.001597638,0.00001014692,0.0000234202,0.0006489838,0.00002263286,0.01162738,0.9490268,0.0003214397,0.0365369,0.00002457842],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.224473,0.001317032,0.7036625,0.0005303365,0.0004646063,0.0004459795,0.004348923,0.02192708,0.04283056],"genre_scores_gemma":[0.3286095,0.0007250905,0.6160614,0.0002177017,0.00009251306,0.0003219152,0.002091359,0.0009768127,0.05090361],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01414419,"threshold_uncertainty_score":0.04731703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007546501820405006,"score_gpt":0.2123202988784446,"score_spread":0.2047737970580396,"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."}}