{"id":"W2573602261","doi":"10.1109/icsens.2016.7808584","title":"A vector light detector for proximity sensing applications","year":2016,"lang":"en","type":"article","venue":"","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photodiode; Computer science; Detector; Pixel; Photodetector; Pyramid (geometry); Automation; Electronic engineering; Artificial intelligence; Optics; Optoelectronics; Materials science; Engineering; Physics; Mechanical engineering; Telecommunications","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":[0.00003136101,0.0000726937,0.00006854239,0.00002707605,0.00003899105,0.00001492343,0.00004521712,0.00002654791,0.00002925013],"category_scores_gemma":[0.00001569186,0.00004877513,0.000039611,0.0000586496,0.00001067952,0.00006259444,0.000006541371,0.00002295349,0.00007349576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003433033,"about_ca_system_score_gemma":0.000005421536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001706428,"about_ca_topic_score_gemma":0.000008809192,"domain_scores_codex":[0.9996141,0.000002768089,0.00008323878,0.0001031,0.00004128079,0.0001555131],"domain_scores_gemma":[0.9997126,0.00005538606,0.000007662002,0.0001543261,0.00002740788,0.00004259271],"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":[0.000004552394,0.00001027855,0.00008696939,0.00005750678,0.00002659697,4.847979e-7,0.0000602813,0.00002507632,0.830403,0.001841881,0.003895845,0.1635875],"study_design_scores_gemma":[0.0005146588,0.00001415929,0.0005618342,0.0000350263,0.00002017896,0.00001026319,0.00002224018,0.01038159,0.6384118,0.001257205,0.3484537,0.0003173714],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06109522,0.00006846586,0.927577,0.001244553,0.0002099536,0.0006410322,0.00001895119,0.001414336,0.00773045],"genre_scores_gemma":[0.9875648,0.000003753765,0.01096201,0.00003767169,0.0001800097,0.00004694483,7.402683e-7,0.00002705509,0.001176993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9264696,"threshold_uncertainty_score":0.1988992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007380335783191618,"score_gpt":0.2031009721560344,"score_spread":0.1957206363728428,"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."}}