{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001589416,0.001009006,0.0005552566,0.0008976207,0.001726842,0.005934925,0.002030583,0.002218718,0.02007955],"category_scores_gemma":[0.00360793,0.0004305013,0.0007974614,0.0005550989,0.001354682,0.003469657,0.003678311,0.002983596,0.006894073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00175017,"about_ca_system_score_gemma":0.001233735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005006299,"about_ca_topic_score_gemma":0.00125603,"domain_scores_codex":[0.9989703,0.0002379829,0.00002731553,0.0003166206,0.0002978031,0.0001500562],"domain_scores_gemma":[0.9975425,0.0007479973,0.00007992547,0.0001468923,0.0002646514,0.001218063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0005537283,0.002648337,0.006782169,0.001055108,0.00007599717,0.001993216,0.0166086,0.009196951,0.09658968,0.1379011,0.2894223,0.4371728],"study_design_scores_gemma":[0.0000836675,0.000620997,0.002757928,0.0003094174,0.00003356374,0.001191799,0.001640978,0.0125438,0.02193818,0.09132696,0.8674611,0.00009153672],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2265051,0.005484559,0.4600195,0.05831505,0.005238366,0.0007334924,0.0018744,0.01198393,0.2298455],"genre_scores_gemma":[0.377298,0.003126388,0.4231519,0.006421147,0.001657786,0.0005717037,0.002635918,0.002643627,0.1824936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02007955,"threshold_uncertainty_score":0.06717283,"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."}}