{"id":"W4255414299","doi":"10.1145/3338286.3344413","title":"WatchPen","year":2019,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Stylus; Smartwatch; Computer science; Human–computer interaction; Context (archaeology); Selection (genetic algorithm); Capacitive sensing; Expression (computer science); Multimedia; Artificial intelligence; Computer vision; Embedded system; Wearable computer; Programming language","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":[0.0002432199,0.0007023511,0.0003313939,0.0006720924,0.0004812532,0.001611854,0.0008717157,0.0007744425,0.2061381],"category_scores_gemma":[0.001116574,0.0003091882,0.0003346526,0.000499818,0.0003296548,0.001834288,0.002169983,0.0007263705,0.06006037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002248915,"about_ca_system_score_gemma":0.0003177456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006308639,"about_ca_topic_score_gemma":0.001597692,"domain_scores_codex":[0.9997609,0.00003400947,0.00001094649,0.00005955343,0.00009980441,0.00003479311],"domain_scores_gemma":[0.9995773,0.0001136457,0.00001584311,0.0001314319,0.00008810736,0.00007367622],"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":[0.0006598888,0.0001316599,0.001144413,0.001122262,0.00003694623,0.001662931,0.001155419,0.002458203,0.1042803,0.1066164,0.2902702,0.4904613],"study_design_scores_gemma":[0.00002499039,0.00007065862,0.0005237612,0.00006164224,0.00001035745,0.0005084091,0.00008918459,0.002988216,0.01566975,0.003860912,0.9761703,0.00002182213],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0161316,0.001445143,0.3546665,0.001146853,0.001470839,0.0003787525,0.004905273,0.04382933,0.5760257],"genre_scores_gemma":[0.1233315,0.00201718,0.1171978,0.001218606,0.0002537456,0.0005309573,0.00803645,0.007611185,0.7398027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2061381,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003730243681098213,"score_gpt":0.2101778890016931,"score_spread":0.2064476453205949,"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."}}