{"id":"W1481096275","doi":"10.34105/j.kmel.2014.06.002","title":"Use of global context for handling noisy names in discussion texts of a homeopathy discussion forum","year":2014,"lang":"en","type":"article","venue":"Knowledge Management & E-Learning An International Journal","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University","keywords":"Context (archaeology); Computer science; Task (project management); Homeopathy; Natural language processing; Artificial intelligence; Information retrieval; Linguistics; History; Medicine; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000881324,0.0001577018,0.0002578866,0.0003043019,0.000117404,0.000243115,0.001090643,0.00005291314,0.000009090265],"category_scores_gemma":[0.0002366703,0.0001009606,0.0001470327,0.0001912096,0.00003666526,0.000976379,0.0005276378,0.0002085616,0.000004227288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001626803,"about_ca_system_score_gemma":0.00003884755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001574113,"about_ca_topic_score_gemma":0.0000820994,"domain_scores_codex":[0.9981167,0.0002004302,0.0006318666,0.000328327,0.0004581785,0.0002644963],"domain_scores_gemma":[0.9988181,0.00009156806,0.0004442091,0.0002470604,0.0003080534,0.00009099899],"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":[0.00009912021,0.0002963582,0.06618185,0.00005978701,0.00007385015,0.000007972265,0.001674358,0.01010313,0.0001544514,0.08345449,0.0001343156,0.8377603],"study_design_scores_gemma":[0.002375936,0.0004218968,0.03077171,0.001335385,0.00002602816,0.00002878441,0.001702129,0.9342504,0.0002103505,0.009257104,0.0193206,0.000299714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1073095,0.00007288598,0.8888966,0.001129857,0.001180023,0.0001860398,0.000002964609,0.00003539578,0.001186724],"genre_scores_gemma":[0.9687853,0.00002911346,0.03030134,0.00003958502,0.0001594976,0.00001004745,0.00000519005,0.00001116093,0.0006587494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9241472,"threshold_uncertainty_score":0.4117053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02262400135493118,"score_gpt":0.2942936209702371,"score_spread":0.2716696196153059,"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."}}