{"id":"W2165706283","doi":"10.5555/602099.602136","title":"GeneVis: visualization tools for genetic regulatory network dynamics","year":2002,"lang":"en","type":"article","venue":"IEEE Visualization","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Visualization; Computer science; Focus (optics); Process (computing); Information visualization; Data visualization; Human–computer interaction; 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":[0.001399013,0.002062544,0.001116301,0.00294489,0.0008126368,0.002590419,0.002780568,0.001700113,0.05276526],"category_scores_gemma":[0.006173352,0.001189472,0.001330799,0.002354641,0.0005467912,0.00340663,0.003051002,0.00286375,0.008985649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007801285,"about_ca_system_score_gemma":0.001046708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003926962,"about_ca_topic_score_gemma":0.003767727,"domain_scores_codex":[0.9993217,0.0001569827,0.00007025636,0.00007602423,0.0003057795,0.00006917532],"domain_scores_gemma":[0.9982627,0.001007373,0.00009470271,0.0001783915,0.0003183555,0.0001385128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006908973,0.0002360523,0.001642586,0.001880539,0.0002510739,0.001035515,0.001618929,0.04115468,0.01977596,0.06977972,0.5582736,0.3036605],"study_design_scores_gemma":[0.0007065622,0.0001139841,0.001398306,0.0004520843,0.0001056267,0.0009268115,0.0002831624,0.3380828,0.02617602,0.1008101,0.5306799,0.0002646305],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00281635,0.0007219009,0.7324495,0.000681531,0.0002060715,0.000232736,0.01084313,0.2447197,0.007329185],"genre_scores_gemma":[0.06014462,0.002588585,0.8522432,0.0006181765,0.0001579042,0.001993652,0.02574115,0.04411982,0.01239286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05276526,"threshold_uncertainty_score":0.1765174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03905607807491299,"score_gpt":0.3019224198744776,"score_spread":0.2628663417995646,"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."}}