{"id":"W2952651124","doi":"10.1145/3331156","title":"Tangible BioNets","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada; Canada Foundation for Innovation; Ontario Ministry of Research, Innovation and Science; Canada Research Chairs; National Science Foundation","keywords":"Computer science; Usability; Process (computing); Biological network; Human–computer interaction; Biological data; Data science; Bioinformatics","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":[0.001219541,0.001290946,0.0006213641,0.001011899,0.0006948155,0.002786679,0.001796347,0.001244414,0.0493182],"category_scores_gemma":[0.005857725,0.000605623,0.001058745,0.0006502431,0.0008481374,0.003314497,0.004643514,0.0008016126,0.007306623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005186252,"about_ca_system_score_gemma":0.0006270572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009207745,"about_ca_topic_score_gemma":0.001505167,"domain_scores_codex":[0.9991437,0.0002501674,0.00006277047,0.0001775041,0.0002882358,0.00007754372],"domain_scores_gemma":[0.9978697,0.001157594,0.0001153249,0.0004243622,0.0002313638,0.0002016694],"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":[0.002107672,0.0004673246,0.005754062,0.005068731,0.0002523984,0.002227732,0.006823884,0.0448551,0.1168634,0.1486837,0.09044673,0.5764493],"study_design_scores_gemma":[0.0003004157,0.001096281,0.006522843,0.000883082,0.0002098716,0.002783795,0.001881644,0.1313522,0.04546193,0.09714034,0.712121,0.0002468034],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03263199,0.001038646,0.8975414,0.000787493,0.0004405559,0.0006146991,0.003902687,0.02441583,0.03862668],"genre_scores_gemma":[0.3580616,0.001969876,0.5794507,0.0009449169,0.0001163857,0.002315504,0.007045017,0.004041206,0.04605483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0493182,"threshold_uncertainty_score":0.1649858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04530912568224608,"score_gpt":0.3326112583374098,"score_spread":0.2873021326551637,"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."}}