{"id":"W4412271096","doi":"","title":"Arb-Med and Med-Arb in Commercial Cases: the European and Asian Approaches","year":2015,"lang":"en","type":"article","venue":"Research at the University of Copenhagen (University of Copenhagen)","topic":"EU Law and Policy Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre for International Governance Innovation","funders":"","keywords":"Information retrieval; Computer science","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":[0.02146978,0.0007914945,0.001314914,0.004020328,0.00486515,0.01532994,0.004428487,0.008737765,0.01437552],"category_scores_gemma":[0.03206389,0.0006696905,0.001232421,0.00564429,0.02682698,0.0190987,0.008270921,0.009217606,0.0006118261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008622319,"about_ca_system_score_gemma":0.006249436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01615208,"about_ca_topic_score_gemma":0.02815956,"domain_scores_codex":[0.9836553,0.01037922,0.0004697706,0.0009901619,0.00149842,0.00300711],"domain_scores_gemma":[0.9734095,0.019602,0.002253349,0.001487604,0.001799378,0.001448025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000387722,0.000034863,0.000808607,0.00003358582,0.00001771456,0.0001170914,0.002540525,0.0002657274,0.00002046141,0.9929595,0.0007579011,0.002405168],"study_design_scores_gemma":[0.0001231813,0.00009358348,0.006665225,0.0006072465,0.0001842435,0.0003872175,0.03762482,0.005080656,0.000368389,0.929323,0.01947318,0.00006929904],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.1698335,0.007056089,0.01160358,0.02486072,0.000200237,0.0001143167,0.0001082523,0.00003736592,0.7861859],"genre_scores_gemma":[0.9874313,0.001122056,0.001193398,0.002189187,0.0001360808,0.00009751735,0.00002748899,0.00002142003,0.007781464],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.02146978,"threshold_uncertainty_score":0.1135443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2261761012617862,"score_gpt":0.3354805085268803,"score_spread":0.1093044072650941,"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."}}