{"id":"W4401136159","doi":"10.1484/m.sem-eb.5.140476","title":"Front matter (“Contents”, “List of Abbreviations”, “List of Illustrations”, “List of Colour Plates”, “Editors’ Preface”, “Foreword”)","year":2007,"lang":"en","type":"paratext","venue":"Studies in the early Middle Ages","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Front (military); Information retrieval; Computer science; Library science; Geography; Meteorology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001903889,0.0005195561,0.002030462,0.0006669905,0.0002476352,0.000065512,0.001059722,0.0003285556,0.007285653],"category_scores_gemma":[0.0003717219,0.0004565614,0.0004738074,0.0004576076,0.001674208,0.000281614,0.0004197734,0.000432675,0.004931394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000210845,"about_ca_system_score_gemma":0.00004253012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002426551,"about_ca_topic_score_gemma":0.000513368,"domain_scores_codex":[0.99584,0.00008015597,0.002593255,0.0007509618,0.0001900327,0.0005455608],"domain_scores_gemma":[0.9954357,0.0006035181,0.002680784,0.0009008208,0.0003279316,0.00005130502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006332183,0.0002696391,0.02244539,0.0009156192,0.001229842,0.000003348042,0.012663,0.0001659285,0.000008147534,0.003729486,0.9584547,0.00005158159],"study_design_scores_gemma":[0.003050581,0.0006184282,0.05551473,0.002299266,0.0004044589,0.00000683084,0.04653573,0.00007072819,0.0004797845,0.004591349,0.8845196,0.001908478],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1257383,0.06230512,0.00008998918,0.0005169999,0.0285272,0.001862028,0.0127947,0.00002167337,0.768144],"genre_scores_gemma":[0.5145169,0.006788201,0.0002993272,0.0001517272,0.001135305,0.0001413291,0.0001929714,0.00005952198,0.4767147],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.3887786,"threshold_uncertainty_score":0.9997886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1213416081848569,"score_gpt":0.2812765236486056,"score_spread":0.1599349154637487,"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."}}