{"id":"W2028364300","doi":"10.1063/1.3292427","title":"SuperCDMS Detector Fabrication Advances","year":2009,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Spectroscopy and Laser Applications","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy; National Science Foundation","keywords":"Fabrication; Detector; Scalability; Optoelectronics; Yield (engineering); Ionization; Materials science; Nanotechnology; Computer science; Physics; Optics; Database","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":[0.002546191,0.0007742229,0.0005952746,0.0008680323,0.0006400138,0.001344782,0.001747037,0.0009310973,0.01175693],"category_scores_gemma":[0.002782757,0.000591518,0.0007358561,0.000774631,0.0002864813,0.001854025,0.001440779,0.001159016,0.006761568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002393287,"about_ca_system_score_gemma":0.001636389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002781483,"about_ca_topic_score_gemma":0.004388343,"domain_scores_codex":[0.9983923,0.0001281488,0.00005501507,0.0003401586,0.001012193,0.00007218508],"domain_scores_gemma":[0.9974402,0.0003320203,0.0001716192,0.0005218388,0.001385499,0.0001488188],"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":[0.0002867285,0.0001716701,0.006363367,0.0007947256,0.0001566833,0.0002838933,0.0003750377,0.004845528,0.6425515,0.03888756,0.1114015,0.1938818],"study_design_scores_gemma":[0.00004731949,0.0001867512,0.004618579,0.0001023518,0.00007720353,0.000940205,0.0001185609,0.023218,0.3650521,0.003355141,0.6022029,0.00008096787],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2678976,0.02179853,0.4088,0.02218638,0.008664845,0.0008506842,0.01354622,0.01749249,0.2387633],"genre_scores_gemma":[0.3599256,0.01027508,0.5358796,0.003747946,0.001728509,0.0005641938,0.01037052,0.001668898,0.07583965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01175693,"threshold_uncertainty_score":0.03933084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436631706641718,"score_gpt":0.2664932416308356,"score_spread":0.2521269245644184,"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."}}