{"id":"W2618033904","doi":"10.16995/dm.58","title":"From \"anhelitus\" to \"hanellissement:\" Cross-referencing in the Anglo-Norman dictionary","year":2015,"lang":"en","type":"article","venue":"Digital Medievalist","topic":"Lexicography and Language Studies","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digitization; Computer science; Machine-readable dictionary; Artificial intelligence; Natural language processing; Information retrieval; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002017292,0.0001505079,0.0001397548,0.00008498091,0.0001905564,0.0006750661,0.0002578085,0.00002664616,0.0002304393],"category_scores_gemma":[0.0001286944,0.00009717027,0.00006784018,0.00009027592,0.0001925409,0.0004893048,0.00007898873,0.000118174,0.0001928723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003469766,"about_ca_system_score_gemma":0.00002195012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002004193,"about_ca_topic_score_gemma":0.002793937,"domain_scores_codex":[0.9989232,0.00003150968,0.0002295771,0.0002055829,0.0003672667,0.0002428466],"domain_scores_gemma":[0.9994406,0.0001075556,0.00004253844,0.0002126202,0.00009917354,0.00009750482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001388985,0.0002808189,0.01687882,0.00002859098,0.0001108724,0.0001575717,0.8802835,0.00001755734,0.00000744542,0.02937039,0.06433607,0.008389428],"study_design_scores_gemma":[0.0004630346,0.0001414464,0.006119335,0.00006543612,0.00001543338,0.000003300878,0.1005546,0.000009946544,0.00001902077,0.006205517,0.8861696,0.0002333243],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6127119,0.0007630294,0.000006653725,0.0006529268,0.0008093939,0.000177118,0.00042029,0.00006882496,0.3843898],"genre_scores_gemma":[0.995308,0.000004845064,0.000009304237,0.001590586,0.001520896,0.0000361269,0.0001770552,0.00001127738,0.001341936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8218335,"threshold_uncertainty_score":0.6509678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07115569016273095,"score_gpt":0.2924588817610426,"score_spread":0.2213031915983117,"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."}}