{"id":"W2888194409","doi":"10.1145/3229434.3229482","title":"Multiplexing spatial memory","year":2018,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Multiplexing; Set (abstract data type); Spatial multiplexing; Interference (communication); Human–computer interaction; Smartwatch; Embedded system; Wearable computer; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001089294,0.001127484,0.0006161882,0.001439909,0.0005052516,0.002113815,0.002178258,0.0008023359,0.0320369],"category_scores_gemma":[0.01002955,0.0007164914,0.000586077,0.001255591,0.0006483535,0.006188457,0.003640688,0.0006442119,0.003278428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003717955,"about_ca_system_score_gemma":0.000390212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009558581,"about_ca_topic_score_gemma":0.001419102,"domain_scores_codex":[0.9991941,0.0001686586,0.0001011738,0.0001594077,0.0002191187,0.0001575818],"domain_scores_gemma":[0.986218,0.00635675,0.0006912005,0.004419683,0.001737892,0.0005765192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002409779,0.0005204654,0.007829906,0.001200187,0.0001635641,0.0005839213,0.001286987,0.004095773,0.2423348,0.01054292,0.007620793,0.7214109],"study_design_scores_gemma":[0.0006832256,0.007718396,0.0319212,0.0007344285,0.0007598795,0.005945701,0.002201046,0.0862381,0.6801303,0.03050136,0.1525305,0.0006359217],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4252478,0.002743396,0.5233703,0.0006376203,0.0004315453,0.0006176781,0.00138223,0.01499598,0.03057342],"genre_scores_gemma":[0.7945207,0.0007539269,0.1894047,0.0003993784,0.0001145708,0.0005175683,0.0006026306,0.0008190201,0.0128675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0320369,"threshold_uncertainty_score":0.1071741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01367663686809096,"score_gpt":0.260930124351887,"score_spread":0.2472534874837961,"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."}}