{"id":"W3121833776","doi":"10.4271/2021-01-0063","title":"Intelligent Voice Activated Drone(s) for in-Vehicle Services and Real-Time Predictions","year":2021,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Drone; Computer science; Voice communication; Real-time computing; Aeronautics; Computer network; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001812139,0.0004507919,0.0002392248,0.0003150291,0.0001391623,0.0004932776,0.0006562552,0.0003323919,0.005970856],"category_scores_gemma":[0.0005917366,0.0001675209,0.0001774403,0.0001211831,0.0001894764,0.0004564956,0.0003803168,0.0002960401,0.001627783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001404233,"about_ca_system_score_gemma":0.0001702731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001875804,"about_ca_topic_score_gemma":0.001981847,"domain_scores_codex":[0.9998205,0.00002517063,0.00001093235,0.00004630171,0.00007673546,0.00002035342],"domain_scores_gemma":[0.9997324,0.00007200128,0.00002883444,0.00005864385,0.00007977161,0.00002833617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00156277,0.0006499462,0.01294146,0.0003682863,0.0001231877,0.001110013,0.0008601504,0.04238032,0.4577608,0.005131581,0.01504895,0.4620626],"study_design_scores_gemma":[0.0002118971,0.001206336,0.008274177,0.00007757592,0.0001471992,0.0009496397,0.0002136012,0.6957749,0.2357616,0.001190042,0.05609194,0.0001011538],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3279415,0.0004571231,0.59093,0.0002596628,0.0002790055,0.0005167533,0.0005783781,0.05003377,0.02900377],"genre_scores_gemma":[0.9304414,0.000183051,0.05450245,0.000177276,0.00004420626,0.00008620913,0.0003030096,0.0003299712,0.01393236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005970856,"threshold_uncertainty_score":0.01997453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007829126624687995,"score_gpt":0.2273303324889943,"score_spread":0.2195012058643064,"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."}}