{"id":"W7132572350","doi":"","title":"Infiniti: Taking on the German Big Three (B)","year":2017,"lang":"","type":"other","venue":"CEIBS Institutional Repository","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Casa","funders":"","keywords":"German; Subject (documents); Government (linguistics)","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":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"category_scores_codex":[0.001471209,0.002108199,0.001349554,0.001056898,0.01147111,0.001454497,0.004422672,0.00183056,0.003720353],"category_scores_gemma":[0.002386258,0.001662202,0.001187868,0.0004100724,0.009276253,0.0004672327,0.001021539,0.003525395,0.03987839],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002657487,"about_ca_system_score_gemma":0.006726277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005067348,"about_ca_topic_score_gemma":0.000413406,"domain_scores_codex":[0.9903032,0.0006375654,0.001458707,0.00236216,0.003639408,0.001598989],"domain_scores_gemma":[0.9869671,0.0008380814,0.005121319,0.005814885,0.0006681558,0.0005904703],"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":[0.0006557353,0.001256491,0.001684148,0.0004688039,0.003113828,0.006175597,0.0005428409,0.000598889,0.009394555,0.7351816,0.2239427,0.01698473],"study_design_scores_gemma":[0.001081681,0.0001784662,0.01629585,0.006790345,0.0005031933,0.001416662,0.00002038279,0.000273335,0.001480217,0.002256761,0.9679604,0.00174266],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.008354267,0.00383447,0.0002331649,0.0002547903,0.02380354,0.001991446,0.0006377929,0.0004100897,0.9604805],"genre_scores_gemma":[0.654816,0.0001725328,0.0001052869,0.0007142233,0.01813668,0.000412715,0.0001854274,0.001079587,0.3243775],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7440177,"threshold_uncertainty_score":0.9995821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06494586925924034,"score_gpt":0.2951368108136561,"score_spread":0.2301909415544157,"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."}}