{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001706833,0.0001238506,0.000141929,0.000171265,0.00009174518,0.0002375606,0.002492762,0.00004420007,0.0000527254],"category_scores_gemma":[0.00005280075,0.00009064163,0.0001020939,0.0003617664,0.00002058554,0.0009255819,0.001068715,0.0001440648,0.0002193335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000480457,"about_ca_system_score_gemma":0.000009416091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007499655,"about_ca_topic_score_gemma":4.732912e-7,"domain_scores_codex":[0.9989662,0.000007480169,0.0002556671,0.0003117602,0.0003086115,0.0001502663],"domain_scores_gemma":[0.9988478,0.0000366429,0.0002811537,0.0006024861,0.000195768,0.00003614227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004075186,0.000696889,0.00964612,0.0002635212,0.0001442069,8.639019e-7,0.002184852,0.0005061111,0.1127599,0.6701329,0.1735116,0.0301122],"study_design_scores_gemma":[0.001885414,0.001255747,0.01982279,0.001157308,0.00004962906,0.00006900433,0.0002555738,0.4161047,0.4037214,0.03970966,0.1149273,0.001041487],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.956494,0.00001098348,0.01641854,0.003828031,0.003397646,0.0004718812,0.00000414577,0.0003232132,0.01905157],"genre_scores_gemma":[0.9903113,0.000003477868,0.007187407,0.0008812103,0.0001540915,0.000003342396,0.000003046367,0.00001114719,0.001445017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6304233,"threshold_uncertainty_score":0.4632214,"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."}}