{"id":"W6912022072","doi":"10.5281/zenodo.15564300","title":"Working with Historical Textual Data: Preliminary Results from Applying Survival Analysis to the Old English Poetic Corpus","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Poetry; Obsolescence; Syllable; Vocabulary; Stress (linguistics); Lexical analysis; Linguistic analysis","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0010011,0.00009530051,0.0001367985,0.0001412097,0.003291605,0.0006878399,0.001448543,0.00005531928,0.0007648747],"category_scores_gemma":[0.002074628,0.00007067525,0.00003819412,0.002111972,0.0001115492,0.0002219881,0.001006908,0.0002176382,0.0003919127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004397534,"about_ca_system_score_gemma":0.00001122127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003404323,"about_ca_topic_score_gemma":0.000412483,"domain_scores_codex":[0.9979678,0.0005645746,0.0002008189,0.0004625728,0.0005114579,0.0002928033],"domain_scores_gemma":[0.9986798,0.0001119494,0.00008041625,0.000612793,0.0003974899,0.0001176077],"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":[0.001440394,0.0002456134,0.0001776516,0.00001351214,0.0007467527,0.0000423151,0.1085787,0.0009943672,0.0003255726,0.01366349,0.5922855,0.2814862],"study_design_scores_gemma":[0.0002558874,0.0000767505,0.001543692,0.00002251257,0.0001466114,6.591018e-7,0.01096105,0.0002455701,0.000006645121,0.00002541373,0.9866102,0.0001049579],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08808984,0.001199302,0.03247908,0.0201309,0.001708285,0.00309998,0.001186989,0.002859473,0.8492461],"genre_scores_gemma":[0.9869964,0.00005047293,0.0002251357,0.0002145079,0.0005328591,1.440052e-7,0.002312916,0.0001495871,0.009518044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8989065,"threshold_uncertainty_score":0.998006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06234899330498152,"score_gpt":0.2752732445029575,"score_spread":0.212924251197976,"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."}}