{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002225984,0.00008723814,0.0001314985,0.00001083742,0.0001404388,0.0002193312,0.0009502962,0.00003865558,0.00009864711],"category_scores_gemma":[0.0003082176,0.00005313826,0.00007260765,0.00006997357,0.00005421479,0.0002889058,0.0004170953,0.0001730458,0.000002925166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001993775,"about_ca_system_score_gemma":0.00008940625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003073319,"about_ca_topic_score_gemma":0.00004241132,"domain_scores_codex":[0.9989908,0.0000116688,0.0001913287,0.000225055,0.0004741883,0.0001070084],"domain_scores_gemma":[0.9991229,0.0002287217,0.0001772149,0.00007513413,0.0003725668,0.00002351321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001257312,0.00006977988,0.003826943,0.00001199578,0.0004823599,0.000001920918,0.01118878,0.000003967337,0.01704577,0.9240224,0.001101132,0.04223239],"study_design_scores_gemma":[0.002331148,0.00006178107,0.03064359,0.0004374358,0.0005312993,0.00002067116,0.02031456,0.1115799,0.06655782,0.7403829,0.02623001,0.0009088241],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.946866,0.0001435252,0.0007053417,0.0387779,0.001097406,0.00008749562,0.00002439833,0.00003114003,0.01226684],"genre_scores_gemma":[0.9969469,0.00005852529,0.001879874,0.0006649012,0.000210632,0.000009105824,0.000001478103,0.000003704689,0.0002248196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1836395,"threshold_uncertainty_score":0.2166915,"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."}}