{"id":"W4286219887","doi":"10.1002/essoar.10510978.2","title":"m-NLP inference models using simulation and regression techniques","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Norges Forskningsråd; H2020 European Research Council; China Scholarship Council; Compute Canada","keywords":"Preprint; Inference; Computer science; World Wide Web; Information retrieval; Natural language processing; Artificial intelligence","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":[0.01358492,0.001581919,0.00232077,0.002343909,0.001312666,0.003285779,0.003786146,0.002740642,0.01060742],"category_scores_gemma":[0.0726686,0.001611042,0.002901943,0.003024154,0.001473301,0.004051697,0.002524454,0.00516212,0.003729399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002091857,"about_ca_system_score_gemma":0.003683204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02926517,"about_ca_topic_score_gemma":0.02801411,"domain_scores_codex":[0.9911987,0.006438192,0.0003032613,0.001172704,0.0005906453,0.0002963713],"domain_scores_gemma":[0.927499,0.06497014,0.001508153,0.003087359,0.002436521,0.0004988175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003156986,0.0001503999,0.004821579,0.0003299632,0.0004672014,0.0002172415,0.0002235559,0.7921678,0.0002791533,0.09672404,0.01607584,0.0882275],"study_design_scores_gemma":[0.00003291301,0.00001006981,0.0001655058,0.00002380363,0.00002146211,0.00001318408,0.00001517852,0.9552113,0.0001159609,0.04299266,0.001387804,0.00001024914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008928997,0.0008008593,0.9831692,0.001311428,0.0001446517,0.0001374809,0.001352855,0.001834439,0.002320104],"genre_scores_gemma":[0.3216966,0.001584819,0.6514734,0.0008379227,0.0006562314,0.001523721,0.008469348,0.001525977,0.01223201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02926517,"threshold_uncertainty_score":0.07184476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1589566516328696,"score_gpt":0.3855688162371672,"score_spread":0.2266121646042976,"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."}}