{"id":"W2395465087","doi":"10.1145/2858036.2858052","title":"DualKey","year":2016,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Selection (genetic algorithm); Process (computing); Term (time); Artificial intelligence; Key (lock); Identification (biology); Computer vision; Machine learning; Operating system","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00002614732,0.00002829769,0.00002533285,0.00001972843,0.00001866377,0.00001508064,0.0002249075,0.000008194731,0.0002785084],"category_scores_gemma":[0.00001360162,0.00001418533,0.00002074809,0.00004123023,0.000007994031,0.000397078,0.0000648391,0.00001049103,0.002019204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008617223,"about_ca_system_score_gemma":0.000007655299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003923334,"about_ca_topic_score_gemma":6.633953e-7,"domain_scores_codex":[0.9997268,0.000007755436,0.00003459523,0.00009648094,0.00004898372,0.00008538759],"domain_scores_gemma":[0.9997451,0.00003604616,0.000009263265,0.0001530998,0.00003444433,0.00002207762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000124857,0.00001175315,0.000369296,3.032257e-7,0.000004721614,0.00000401314,0.00006099128,2.325237e-8,0.266015,0.6894538,0.02906895,0.01500987],"study_design_scores_gemma":[0.0003453779,0.00008380304,0.02537798,0.00001361582,0.000001117152,0.00001503866,0.00002512981,0.0001766712,0.8474491,0.008890835,0.1174525,0.0001688704],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004157194,0.000003661297,0.8268465,0.003163429,0.0001833841,0.00001463932,2.554866e-7,0.00001499153,0.165616],"genre_scores_gemma":[0.9841529,0.000002006073,0.002235814,0.00118453,0.00002016626,0.00000145155,5.115514e-8,0.000001220055,0.01240193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9799957,"threshold_uncertainty_score":0.9987578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008056093002512487,"score_gpt":0.2250448472566883,"score_spread":0.2169887542541759,"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."}}