{"id":"W57230347","doi":"10.1007/978-3-642-23765-2_24","title":"“Oh Snap” – Helping Users Align Digital Objects on Touch Interfaces","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Networks of Centres of Excellence of Canada; Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Snap; Computer science; Translation (biology); Gesture; Rotation (mathematics); Text entry; Jump; Human–computer interaction; Computer vision; Computer graphics (images); Physics","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":[0.0003881577,0.001377699,0.0004367197,0.0004814,0.0005575282,0.001061273,0.001653435,0.001231306,0.02538498],"category_scores_gemma":[0.001314753,0.000405913,0.0004105635,0.0003787005,0.0006211933,0.002418104,0.002796646,0.0007763522,0.007287576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001295001,"about_ca_system_score_gemma":0.0003253859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007344371,"about_ca_topic_score_gemma":0.001638463,"domain_scores_codex":[0.9997175,0.00005456954,0.00001325008,0.00006069053,0.0001146822,0.00003936184],"domain_scores_gemma":[0.9996858,0.0001237772,0.00001511019,0.00005710079,0.00007116442,0.00004712557],"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":[0.000322431,0.0002000404,0.0008900993,0.0007265919,0.00003670763,0.0004552577,0.004189316,0.0009641464,0.1090435,0.005837472,0.04734768,0.8299868],"study_design_scores_gemma":[0.0001871844,0.001773241,0.0147054,0.0007476188,0.0002505958,0.006157948,0.008646148,0.04086578,0.1841491,0.03217009,0.7100276,0.0003193139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09704412,0.002675508,0.7275624,0.0009350878,0.0008275377,0.0004976018,0.000471234,0.03621349,0.133773],"genre_scores_gemma":[0.3149247,0.00287287,0.4885869,0.001081157,0.0002451434,0.0007904994,0.00158928,0.003037142,0.1868723],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02538498,"threshold_uncertainty_score":0.08492124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02177839847595542,"score_gpt":0.2460300935513118,"score_spread":0.2242516950753563,"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."}}