{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001126826,0.002138144,0.001813592,0.0009688046,0.0005841791,0.0006639539,0.001437058,0.0006362374,0.0001424775],"category_scores_gemma":[0.0001851351,0.001995664,0.0006396166,0.001921937,0.000375595,0.002229111,0.0001694604,0.001583745,0.002264308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001345824,"about_ca_system_score_gemma":0.0003879155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001027528,"about_ca_topic_score_gemma":0.0002002377,"domain_scores_codex":[0.9906144,0.000281545,0.00177379,0.001940856,0.002146272,0.003243109],"domain_scores_gemma":[0.9952778,0.0004922063,0.0006336116,0.001919113,0.0006753757,0.00100189],"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":[0.0005167226,0.0004382601,0.0007648827,0.0006950914,0.001167066,0.0005212485,0.003235012,0.07205122,0.822723,0.001155733,0.09056882,0.006162901],"study_design_scores_gemma":[0.00972636,0.001075435,0.002772897,0.00102481,0.001290344,0.0005842246,0.0002283784,0.2362991,0.2454671,0.0001482537,0.4948852,0.006497953],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9764935,0.002173836,0.005380025,0.005362662,0.002008881,0.001600885,0.0001102272,0.002593797,0.004276205],"genre_scores_gemma":[0.9814368,0.0003288526,0.005647031,0.005895193,0.001622952,0.0001870632,0.0004058128,0.0007379425,0.00373831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.577256,"threshold_uncertainty_score":0.999136,"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."}}