{"id":"W2996757157","doi":"10.1145/3366550.3372253","title":"Holding patterns","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Phone; Task (project management); Dynamic time warping; Human–computer interaction; Context (archaeology); Orientation (vector space); Image warping; Interaction technique; Artificial intelligence; Computer vision; Engineering; Gesture","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":[0.0002829161,0.000689769,0.0006448975,0.0009912895,0.0005945825,0.0009466034,0.0006787598,0.0006177294,0.0195462],"category_scores_gemma":[0.003616105,0.0003375912,0.0003916277,0.001120076,0.0003045532,0.00143355,0.0009501558,0.0004266339,0.007637492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001801796,"about_ca_system_score_gemma":0.0002007395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002847932,"about_ca_topic_score_gemma":0.004463542,"domain_scores_codex":[0.9993192,0.00006620559,0.00005250165,0.0002144456,0.0002408594,0.0001067385],"domain_scores_gemma":[0.9986187,0.0003947826,0.0002234289,0.0002796614,0.0003291919,0.0001541892],"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":[0.002031594,0.0002825741,0.1024461,0.001227736,0.0002640036,0.003062547,0.003306014,0.003935221,0.1911431,0.003571102,0.03022907,0.658501],"study_design_scores_gemma":[0.0001546253,0.00136757,0.7159945,0.0005330121,0.0003868548,0.01276286,0.00526322,0.05309897,0.07990678,0.01365829,0.116537,0.000336237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7921321,0.0034805,0.1282463,0.000740512,0.000495899,0.0004482316,0.01071999,0.00424936,0.05948712],"genre_scores_gemma":[0.9435765,0.001222761,0.02546006,0.0003112464,0.00009660418,0.0002039764,0.005590651,0.000462668,0.02307554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0195462,"threshold_uncertainty_score":0.06538856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02311045610533118,"score_gpt":0.2757414154909538,"score_spread":0.2526309593856226,"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."}}