{"id":"W2617362040","doi":"10.1177/0954409717710100","title":"Editorial for JRRT ICRI special edition","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004567308,0.0001483282,0.0003359533,0.00008777093,0.0001298995,0.00003434233,0.0003281251,0.0001587838,0.000004797119],"category_scores_gemma":[0.0002295665,0.0001122276,0.0002280522,0.00005782085,0.0001138964,0.0003598669,0.00001705129,0.000260328,2.090793e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000294363,"about_ca_system_score_gemma":0.00003094522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.964916e-7,"about_ca_topic_score_gemma":5.251165e-7,"domain_scores_codex":[0.9989703,0.000001588831,0.0004751655,0.00008554118,0.000310178,0.0001572805],"domain_scores_gemma":[0.9992369,0.00003302256,0.0002377409,0.00008640065,0.0003147387,0.00009116405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001608418,0.000350832,0.00007674764,0.003921124,0.001284369,0.000003326157,0.00147612,0.2401235,0.4184876,0.1448372,0.1642906,0.02354021],"study_design_scores_gemma":[0.01356696,0.001963656,0.0008479984,0.002959152,0.001170352,0.000258904,0.000439796,0.2047933,0.4334633,0.009813387,0.3294374,0.001285721],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4720371,0.001087067,0.1838211,0.002207794,0.3365634,0.001021949,0.0002851556,0.0001769065,0.002799545],"genre_scores_gemma":[0.9677024,0.0004748296,0.001732464,0.000004679401,0.03004722,0.00000388179,0.000001240123,0.0000184109,0.00001490849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4956653,"threshold_uncertainty_score":0.4576509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008659770352408282,"score_gpt":0.201520838260844,"score_spread":0.1928610679084357,"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."}}