{"id":"W3205836984","doi":"10.16995/dscn.372","title":"Transcribing and Collating for Digital Stemmatology. The Case of Troilus and Criseyde","year":2021,"lang":"en","type":"article","venue":"Digital Studies / Le champ numérique","topic":"Digital Humanities and Scholarship","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Collation; Transcription (linguistics); Computer science; Process (computing); Focus (optics); Linguistics; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001369723,0.0002061248,0.0003920646,0.00003507768,0.0007164461,0.001144674,0.00007721513,0.00003942693,0.000007992914],"category_scores_gemma":[0.0002441369,0.0001528201,0.0001127553,0.00003418845,0.000702283,0.0009452571,0.0001389233,0.0001082078,7.173207e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001606423,"about_ca_system_score_gemma":0.00003583365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002967988,"about_ca_topic_score_gemma":0.001320672,"domain_scores_codex":[0.9989974,0.00002352244,0.0003576331,0.0002733668,0.0000813376,0.0002666963],"domain_scores_gemma":[0.9987756,0.0006167159,0.0001074108,0.0001471023,0.0002970008,0.00005615668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00008784011,0.0001301951,0.0007171077,0.0007116278,0.0005059829,0.0005035364,0.8335102,0.000003040382,0.00002277922,0.1424407,0.0009814266,0.02038562],"study_design_scores_gemma":[0.0005813263,0.0001376386,0.00001546267,0.000102493,0.000001673709,0.0006393167,0.9416931,0.0000305873,0.0001120673,0.01993824,0.03653662,0.0002114271],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.942125,0.00537586,0.00004079611,0.0008020248,0.0001599786,0.0002861532,0.0005568144,0.00003923646,0.05061419],"genre_scores_gemma":[0.9960124,0.0001032313,0.00000410647,0.0001151543,0.0001461183,0.00004556886,0.00002402953,0.00002569854,0.003523671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1225024,"threshold_uncertainty_score":0.9998922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.070871124471773,"score_gpt":0.2621241069930862,"score_spread":0.1912529825213132,"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."}}