{"id":"W2207341472","doi":"10.1016/j.tig.2015.12.002","title":"Visualization: A Mind–Machine Interface for Discovery","year":2015,"lang":"en","type":"article","venue":"Trends in Genetics","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"","keywords":"Biology; Visualization; Interface (matter); Computational biology; Human–computer interaction; Computer science; Artificial intelligence","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":[0.001892156,0.00172899,0.001000873,0.00247424,0.0007599588,0.003461329,0.001933321,0.001923011,0.05190744],"category_scores_gemma":[0.007908216,0.0007107265,0.00138434,0.001394889,0.0006116658,0.003562935,0.003517132,0.001647694,0.01077194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004649701,"about_ca_system_score_gemma":0.0009498513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003028014,"about_ca_topic_score_gemma":0.00367373,"domain_scores_codex":[0.9994245,0.0002168527,0.00005654836,0.0001068174,0.0001569476,0.00003839632],"domain_scores_gemma":[0.9968359,0.002304949,0.00009281727,0.0003548187,0.0002421208,0.0001692741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00119321,0.0002162066,0.002878267,0.002165453,0.0005049675,0.0008661377,0.001868973,0.01003252,0.01809877,0.09815536,0.3756456,0.4883747],"study_design_scores_gemma":[0.000366575,0.000124951,0.001737255,0.0004946785,0.000236806,0.0005820622,0.000281407,0.25106,0.02238602,0.33904,0.3835117,0.0001784751],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002226902,0.0004030604,0.8470154,0.0007727951,0.0002122112,0.0001958891,0.005343355,0.1359339,0.007896392],"genre_scores_gemma":[0.06910871,0.0009494736,0.8963631,0.001025507,0.0001968212,0.001301895,0.00885683,0.01092514,0.0112725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05190744,"threshold_uncertainty_score":0.1736477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07604337098844816,"score_gpt":0.3617832749679186,"score_spread":0.2857399039794705,"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."}}