{"id":"W166397155","doi":"10.1609/icwsm.v5i1.14135","title":"Extracting Meta Statements from the Blogosphere","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Conditional random field; Computer science; Blogosphere; Information extraction; Relationship extraction; Statement (logic); Information retrieval; Classifier (UML); Precision and recall; Natural language processing; Artificial intelligence; Context (archaeology); Relation (database); Metadata; World Wide Web; Data mining; The Internet; Linguistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00140324,0.001438448,0.0006110645,0.008618419,0.0008831443,0.001458214,0.0005484534,0.0007828533,0.001928501],"category_scores_gemma":[0.004580291,0.0004779463,0.0008293138,0.005129169,0.0003924447,0.004138171,0.001093366,0.0008429707,0.001622836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005284245,"about_ca_system_score_gemma":0.001249111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002571624,"about_ca_topic_score_gemma":0.004647637,"domain_scores_codex":[0.9991865,0.0001518674,0.00009142993,0.000180284,0.0002920697,0.00009784772],"domain_scores_gemma":[0.9954178,0.002655043,0.000580697,0.0004168285,0.0008044216,0.0001251542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008781211,0.0003727458,0.04288405,0.001421978,0.0001805318,0.002305216,0.002577686,0.008861168,0.06254093,0.01440298,0.04501894,0.8185557],"study_design_scores_gemma":[0.0001547593,0.000480168,0.09096317,0.0005862542,0.0006664547,0.003262011,0.003697158,0.5134283,0.1220455,0.08667924,0.1777639,0.0002730756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3706842,0.004172677,0.5265728,0.002528094,0.0005821948,0.0006956114,0.04419754,0.026455,0.02411193],"genre_scores_gemma":[0.6646082,0.002461676,0.2705358,0.0002536405,0.0006665504,0.000331245,0.0542569,0.0007324346,0.006153635],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008618419,"threshold_uncertainty_score":0.007421136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08824400931823363,"score_gpt":0.3052413327400337,"score_spread":0.2169973234218001,"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."}}