{"id":"W4387335571","doi":"10.11647/obp.0312.05","title":"Chémar / ཕ་མར། / 切玛","year":2023,"lang":"en","type":"book-chapter","venue":"World oral literature series","topic":"Indian and Buddhist Studies","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Narrative; Selection (genetic algorithm); Table (database); Humanities; History; Computer science; Art; Literature; Artificial intelligence","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":[0.0001328423,0.0004421034,0.000168252,0.0005223908,0.002005176,0.002322969,0.0003245822,0.000545385,0.1231062],"category_scores_gemma":[0.0002028198,0.0001744645,0.0001668503,0.0007019627,0.0008434959,0.001337326,0.0007813176,0.001229632,0.04642735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000995487,"about_ca_system_score_gemma":0.0008800359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004720103,"about_ca_topic_score_gemma":0.01495895,"domain_scores_codex":[0.9999074,0.00001349024,0.000002755463,0.00001630745,0.00004376193,0.00001627412],"domain_scores_gemma":[0.9999437,0.00001051237,0.000004303147,0.000005709704,0.00002271942,0.0000129254],"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":[0.00005926793,0.00008647463,0.0003941232,0.0005472234,0.000003813362,0.0004143046,0.004691816,0.0002124909,0.003078781,0.2138661,0.5690433,0.2076023],"study_design_scores_gemma":[0.000001039761,0.000007318091,0.0001821636,0.00004909371,5.24135e-7,0.0001098022,0.0004560707,0.00002799498,0.0003115315,0.0009024313,0.9979498,0.000002284767],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001914015,0.003467723,0.0005826076,0.0007995929,0.0008752981,0.00003297533,0.0001501901,0.0001553047,0.9920222],"genre_scores_gemma":[0.009240865,0.00225223,0.0005784677,0.000306827,0.0001530421,0.0000179608,0.0001427482,0.0001005048,0.9872075],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1231062,"threshold_uncertainty_score":0.4118313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02761877916931079,"score_gpt":0.2086155990476,"score_spread":0.1809968198782892,"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."}}