{"id":"W4402821732","doi":"10.46298/jdmdh.12520","title":"Temporal Sequencing of Documents","year":2024,"lang":"en","type":"article","venue":"Journal of Data Mining & Digital Humanities","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computational biology; Information retrieval; Biology","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003480814,0.0001008679,0.0002194644,0.0002403567,0.00004436528,0.001106651,0.001668345,0.00002613747,0.00001056548],"category_scores_gemma":[0.0001138953,0.00007754767,0.00006574588,0.0001311396,0.00009606294,0.007824013,0.0006005683,0.0001154354,0.000007292272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000382557,"about_ca_system_score_gemma":0.0002113766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001841344,"about_ca_topic_score_gemma":0.000008619695,"domain_scores_codex":[0.9988279,0.00001611844,0.0004842136,0.0001581661,0.0003616651,0.0001519904],"domain_scores_gemma":[0.9990243,0.0002118039,0.0002202421,0.0004185771,0.00009335241,0.00003177128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006421727,0.0002335361,0.01321977,0.001147656,0.00118449,0.003910232,0.03850044,0.00003993919,0.0009391669,0.1613643,0.06773587,0.7116603],"study_design_scores_gemma":[0.004350022,0.008141015,0.01999262,0.01996902,0.0007266733,0.01384713,0.1212333,0.0562746,0.01097432,0.1103235,0.6300892,0.004078514],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8803372,0.008195013,0.0669428,0.0005451368,0.003273624,0.0001077809,0.0001834375,0.0002133612,0.04020167],"genre_scores_gemma":[0.9839544,0.00003482899,0.01539855,0.00003632369,0.0001638438,2.179954e-7,0.00001000726,0.000006847033,0.000394941],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7075818,"threshold_uncertainty_score":0.9999303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1301717141655876,"score_gpt":0.3188513975220842,"score_spread":0.1886796833564967,"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."}}