{"id":"W2528047696","doi":"10.1145/2983310.2989198","title":"Using Area Learning in Spatially-Aware Ubiquitous Environments","year":2016,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Proxemics; Computer science; Human–computer interaction; Ubiquitous computing; Domain (mathematical analysis); Multimedia; Data science","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":[0.0006289312,0.0005445648,0.000351424,0.0004840409,0.001045219,0.00218865,0.001376546,0.0009198954,0.003719948],"category_scores_gemma":[0.001689711,0.0002521432,0.0004481315,0.0004907549,0.001581356,0.003617713,0.004155308,0.0007961596,0.0007680602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000364617,"about_ca_system_score_gemma":0.0005954839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002917112,"about_ca_topic_score_gemma":0.004003028,"domain_scores_codex":[0.9994127,0.0001881444,0.00001567923,0.0001155966,0.0001637023,0.0001042278],"domain_scores_gemma":[0.9994484,0.0001968754,0.00003501534,0.00013891,0.00007074527,0.0001100569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000312153,0.0005352742,0.003794627,0.0003963956,0.00006957885,0.0006118077,0.006152874,0.06867925,0.04098317,0.2418344,0.007923317,0.6287072],"study_design_scores_gemma":[0.0001882892,0.0008478844,0.003148804,0.000194308,0.0001549126,0.001627746,0.003437934,0.3739383,0.04459708,0.3218996,0.2497574,0.0002076758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04827621,0.0004899512,0.9210752,0.0006246667,0.00008193438,0.0001216153,0.0000529971,0.002988763,0.02628856],"genre_scores_gemma":[0.5966745,0.0004714442,0.3940296,0.0002059688,0.00007051453,0.0001656667,0.00007466586,0.0001718918,0.00813584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003719948,"threshold_uncertainty_score":0.0124445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0310667745220893,"score_gpt":0.2645515291039566,"score_spread":0.2334847545818673,"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."}}