{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001587731,0.0006453149,0.0006278071,0.004792945,0.001320749,0.001978841,0.001058673,0.0004989595,0.004829048],"category_scores_gemma":[0.01340539,0.0005462496,0.0006463157,0.00562512,0.0005215108,0.002426075,0.001298346,0.001232258,0.002684092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009721572,"about_ca_system_score_gemma":0.002722812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007525443,"about_ca_topic_score_gemma":0.01784433,"domain_scores_codex":[0.9975433,0.0005386423,0.0002133294,0.0006825143,0.000885337,0.0001368453],"domain_scores_gemma":[0.9926179,0.002355898,0.0005972918,0.001512151,0.002605289,0.0003114649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005584256,0.0002902588,0.01321367,0.0005368675,0.00009751396,0.0003894474,0.001643865,0.02907878,0.02802926,0.04666984,0.02175761,0.8577344],"study_design_scores_gemma":[0.0001018156,0.0002989096,0.0200992,0.0002525951,0.0001998596,0.001479971,0.001526156,0.5393065,0.05295849,0.1644429,0.2191298,0.0002038736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06129764,0.0007969602,0.9139946,0.0004613895,0.0002100967,0.0005105436,0.006611234,0.004411623,0.01170585],"genre_scores_gemma":[0.1955242,0.0004649581,0.7836925,0.0001287568,0.0001433165,0.0005707522,0.01135367,0.0008909656,0.007230924],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007525443,"threshold_uncertainty_score":0.01615477,"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."}}