{"id":"W2576201175","doi":"","title":"Discovering Relevant Hashtags for Health Concepts: A Case Study of Twitter","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thomson Reuters (Canada)","funders":"","keywords":"Computer science; Search engine indexing; Baseline (sea); Cluster analysis; Information retrieval; Social media; Word (group theory); Natural language processing; Artificial intelligence; Data science; World Wide Web; Linguistics","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":[0.001043449,0.0005974857,0.0003556415,0.001213396,0.000900375,0.0006982792,0.0005926235,0.001600899,0.001794291],"category_scores_gemma":[0.006782968,0.0001206273,0.0004324101,0.001350632,0.0005117984,0.001699972,0.0005724159,0.0006224464,0.0006882144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006556788,"about_ca_system_score_gemma":0.0004828962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007676357,"about_ca_topic_score_gemma":0.01541448,"domain_scores_codex":[0.9992856,0.0003423139,0.00005445875,0.0001108475,0.0001473382,0.0000594117],"domain_scores_gemma":[0.995528,0.00359212,0.0002407819,0.0001797508,0.0002848458,0.0001744518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004017401,0.002819116,0.5541112,0.00264989,0.000522812,0.02541698,0.0123565,0.02868693,0.03995613,0.007813854,0.04474626,0.276903],"study_design_scores_gemma":[0.000763478,0.003319522,0.3122294,0.0004430534,0.0006552455,0.01931629,0.04603435,0.4017133,0.05700351,0.01813787,0.1400671,0.0003171248],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9670138,0.00105256,0.01652961,0.003996229,0.0001026325,0.0002509075,0.006347533,0.000288476,0.004418273],"genre_scores_gemma":[0.9751148,0.0005464258,0.01755196,0.0004241671,0.00009432441,0.000103521,0.0034662,0.00004600469,0.002652704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007676357,"threshold_uncertainty_score":0.01526338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1941573157858804,"score_gpt":0.4542740632284499,"score_spread":0.2601167474425695,"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."}}