{"id":"W4231395042","doi":"10.24908/iqurcp.8064","title":"Enhancing Understanding in Interdisciplinary Communicatio","year":2017,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Interdisciplinary Research and Collaboration","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Modalities; Presentation (obstetrics); Modality (human–computer interaction); Order (exchange); Government (linguistics); Cognitive science; Knowledge management; Computer science; Collective intelligence; Sociology; Engineering ethics; Psychology; Engineering; Human–computer interaction; Business; Social science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01100922,0.000819029,0.0005425259,0.00318348,0.00425624,0.01427602,0.001415624,0.004050362,0.007514922],"category_scores_gemma":[0.02764355,0.0003909585,0.0006056391,0.001475498,0.01210273,0.01897022,0.01926952,0.002499411,0.001182685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002593807,"about_ca_system_score_gemma":0.002942758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001020987,"about_ca_topic_score_gemma":0.001034208,"domain_scores_codex":[0.9819308,0.01403392,0.0003566578,0.0009981497,0.001947889,0.0007326176],"domain_scores_gemma":[0.9810632,0.0135953,0.001479492,0.001651266,0.001267737,0.0009430576],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001218407,0.0004144977,0.005429633,0.0009132257,0.00006205252,0.0005237109,0.2989298,0.001546132,0.005216344,0.4662667,0.006228932,0.2143471],"study_design_scores_gemma":[0.00006640473,0.0002568969,0.004681687,0.001109978,0.0001055991,0.0006759705,0.1559209,0.005614257,0.003731539,0.5843556,0.243392,0.00008916621],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2224188,0.005447818,0.1388848,0.03068423,0.0003615993,0.0003600445,0.0000564064,0.000502594,0.6012838],"genre_scores_gemma":[0.9400137,0.00195721,0.04308762,0.001310999,0.0001241751,0.0003267394,0.00005575183,0.000070618,0.01305327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9889908,"threshold_uncertainty_score":0.05822301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4226527526118548,"score_gpt":0.5283611142004022,"score_spread":0.1057083615885474,"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."}}