{"id":"W2955805562","doi":"","title":"Leveraging Biological Inspiration in an Information Visualization Class","year":2016,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Visualization; Class (philosophy); Information visualization; Data visualization; Human–computer interaction; Data science; Information retrieval; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00115304,0.0001081258,0.00009451318,0.0001711055,0.00005919255,0.00007895796,0.000187526,0.0001176498,0.00006198864],"category_scores_gemma":[0.000344852,0.00008848809,0.00002465629,0.0003426627,0.00004706935,0.0006787567,0.00003329069,0.00008893943,0.00004532099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001100868,"about_ca_system_score_gemma":0.00002513278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005143009,"about_ca_topic_score_gemma":0.0001042137,"domain_scores_codex":[0.9988692,0.0003997247,0.0002770546,0.0001411095,0.0001382165,0.0001747343],"domain_scores_gemma":[0.9991162,0.0001931692,0.00004730746,0.0003011051,0.000255815,0.00008641374],"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":[0.00001343163,0.0004042732,0.02333606,0.0002771232,0.0000244217,0.000001342595,0.01673319,0.001085562,0.1101934,0.1170583,0.001033355,0.7298395],"study_design_scores_gemma":[0.002300646,0.000003543953,0.2486153,0.00194578,0.00001437766,0.00001238143,0.0004294894,0.5314918,0.1499255,0.004488544,0.0596642,0.001108523],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5593119,0.00004902492,0.435523,0.001361011,0.0001378732,0.00009097176,0.000003680774,0.0003121219,0.003210398],"genre_scores_gemma":[0.99641,0.0001207534,0.003106317,0.00005498054,0.0000180508,0.00002711371,0.0001490846,0.00001203822,0.000101704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.728731,"threshold_uncertainty_score":0.360844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01421532801596799,"score_gpt":0.2143164205390623,"score_spread":0.2001010925230944,"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."}}