{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.01882285,0.000338189,0.0005913213,0.00182929,0.004057358,0.007516128,0.006262539,0.0002401479,0.0001547211],"category_scores_gemma":[0.007373192,0.0002853745,0.0001377481,0.001653955,0.002376491,0.004276334,0.01051733,0.001787134,0.0005929068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001199588,"about_ca_system_score_gemma":0.0009336775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000254707,"about_ca_topic_score_gemma":0.0033709,"domain_scores_codex":[0.9911859,0.0005455145,0.001237105,0.001243157,0.004291909,0.001496384],"domain_scores_gemma":[0.9919673,0.001527454,0.0005592023,0.001847196,0.003627507,0.0004713264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009225722,0.0003817485,0.03961631,0.0001070857,0.00006362025,0.0001278485,0.03137193,0.000009422604,0.01398514,0.8721951,0.0278102,0.01340908],"study_design_scores_gemma":[0.0006878705,0.0003803037,0.007943924,0.0004538293,0.000003224816,0.00001273486,0.08481395,0.004480455,0.002404664,0.8977312,0.0007496479,0.0003381611],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4236709,0.0001025817,0.01542029,0.3128048,0.0007690312,0.001858712,0.00001707681,0.0001603783,0.2451962],"genre_scores_gemma":[0.9940531,0.0001209279,0.0004562923,0.0000384575,0.0002701956,0.0001816476,0.00000664103,0.00003676703,0.004836006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5703822,"threshold_uncertainty_score":0.9999598,"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."}}