{"id":"W2626955990","doi":"10.3968/9156","title":"Pathology-Related Term-Frequency in Chinese Medical Classics","year":2017,"lang":"en","type":"article","venue":"Canadian social science","topic":"Traditional Chinese Medicine Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vocabulary; General pathology; Clinical pathology; China; TRACE (psycholinguistics); Vernacular; The Internet; Traditional Chinese medicine; Term (time); Word lists by frequency; Medicine; Pathology; Computer science; Linguistics; History; Alternative medicine; Natural language processing; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.001294418,0.0002535341,0.0003543664,0.01384158,0.0007782714,0.001423359,0.0003608277,0.0002556676,0.003825641],"category_scores_gemma":[0.007430015,0.000084559,0.000317131,0.02005406,0.001196325,0.001438933,0.0008582367,0.000277761,0.0002592709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001472005,"about_ca_system_score_gemma":0.001979413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006741173,"about_ca_topic_score_gemma":0.006434531,"domain_scores_codex":[0.9986745,0.0002286974,0.0002967328,0.000186833,0.0005104649,0.0001028282],"domain_scores_gemma":[0.9951207,0.002652149,0.0009715463,0.0002124505,0.0008089227,0.0002341699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009288596,0.0001565229,0.4808431,0.008420005,0.0002816732,0.003971801,0.05865716,0.0005143187,0.01728988,0.01409039,0.007292542,0.4075538],"study_design_scores_gemma":[0.00002755229,0.0002144667,0.9391292,0.0005014485,0.0002417056,0.004487816,0.01255372,0.0007787049,0.002334398,0.003502241,0.03616144,0.0000672308],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9794307,0.006469969,0.001375227,0.0004846927,0.0001131799,0.00009156745,0.002351353,0.00005014067,0.009633213],"genre_scores_gemma":[0.991209,0.00246279,0.00271734,0.00009476211,0.0001212695,0.00007463408,0.001572223,0.00001822506,0.001729727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9861584,"threshold_uncertainty_score":0.01340383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01824139814812461,"score_gpt":0.3214310599775017,"score_spread":0.3031896618293771,"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."}}