{"id":"W4236722613","doi":"10.1093/llc/fql031","title":"Introduction","year":2006,"lang":"en","type":"article","venue":"Digital Scholarship in the Humanities","topic":"Digital Humanities and Scholarship","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Library science; Art history; Selection (genetic algorithm); History; Humanities; Art; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001302512,0.001072191,0.0008229066,0.002347454,0.002163545,0.00755831,0.002055611,0.002194472,0.3949514],"category_scores_gemma":[0.006104045,0.0003382333,0.0007819664,0.00249728,0.000986927,0.004866789,0.003394257,0.002399546,0.2787393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001805599,"about_ca_system_score_gemma":0.002797755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003028604,"about_ca_topic_score_gemma":0.003951203,"domain_scores_codex":[0.9981926,0.000217698,0.0001349972,0.0003590662,0.0009305087,0.0001651866],"domain_scores_gemma":[0.9972956,0.0005081532,0.0001180026,0.0003932181,0.001204188,0.0004807697],"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":[0.00002179402,0.00001916108,0.0001639862,0.0001720687,0.000003470918,0.00005370071,0.0002879895,0.00005098075,0.000180825,0.01257554,0.8948904,0.09158009],"study_design_scores_gemma":[0.000001156449,0.000005160571,0.0001186609,0.00006438449,7.947921e-7,0.00004865241,0.000066398,0.00001312269,0.00002866108,0.001325274,0.9983248,0.000002959405],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"editorial","genre_scores_codex":[0.001281057,0.01559215,0.006612351,0.02743352,0.06967855,0.0002400829,0.007734278,0.002193553,0.8692344],"genre_scores_gemma":[0.004534723,0.008804328,0.003235831,0.005815,0.009856374,0.0001579887,0.00640717,0.0009985409,0.9601901],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.3949514,"threshold_uncertainty_score":0.8630284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04394869842460745,"score_gpt":0.2188178829598543,"score_spread":0.1748691845352468,"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."}}