{"id":"W3189038708","doi":"10.1145/3460881.3460934","title":"PenShaft: Enabling Pen Shaft Detection and Interaction for Touchscreens","year":2021,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of Waterloo; Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Human–computer interaction","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":[0.0009286338,0.001435688,0.0006422915,0.0009994117,0.0003306621,0.001221358,0.001584193,0.0008741094,0.01477161],"category_scores_gemma":[0.00514124,0.0005489541,0.0003953017,0.0004893473,0.0004962263,0.001827005,0.001893027,0.0007739463,0.003322551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002562128,"about_ca_system_score_gemma":0.0003588244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00114075,"about_ca_topic_score_gemma":0.001461852,"domain_scores_codex":[0.9982573,0.0001771124,0.00008347013,0.000213031,0.001014699,0.0002543263],"domain_scores_gemma":[0.9972035,0.001254974,0.0002665869,0.0005224289,0.0005050605,0.0002474364],"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":[0.00128177,0.0004774336,0.004826086,0.001014374,0.0000807688,0.00156179,0.0007698304,0.00237465,0.4679131,0.004801118,0.02736236,0.4875368],"study_design_scores_gemma":[0.0002481129,0.002102922,0.01498363,0.0002955066,0.0001227019,0.006902638,0.0001850779,0.05132053,0.7920401,0.001956696,0.1294334,0.0004087132],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.272716,0.002484862,0.6374749,0.0004191161,0.0006852192,0.001058461,0.001863486,0.0594378,0.02386011],"genre_scores_gemma":[0.7262703,0.001088072,0.2422712,0.0004723123,0.0001731696,0.0004240127,0.001235563,0.002162535,0.02590283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01477161,"threshold_uncertainty_score":0.04941595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02556442194576651,"score_gpt":0.2923758703600274,"score_spread":0.2668114484142609,"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."}}