{"id":"W4306951345","doi":"10.1002/essoar.10510978.3","title":"m-NLP inference models using simulation and regression techniques","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","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":"Inference; Computer science; Regression; Artificial intelligence; Data set; Set (abstract data type); Satellite; Synthetic data; Algorithm; Pattern recognition (psychology); Machine learning; Mathematics; Statistics; Physics","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":[0.004742905,0.001316123,0.001430677,0.001396874,0.0006693728,0.001426473,0.002941437,0.001739801,0.003442137],"category_scores_gemma":[0.02325129,0.001037478,0.001462819,0.001289041,0.0008769262,0.001815153,0.001088351,0.002958571,0.001105055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001195535,"about_ca_system_score_gemma":0.001467772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01759201,"about_ca_topic_score_gemma":0.01404664,"domain_scores_codex":[0.9984881,0.0007572256,0.00009814274,0.0003748488,0.0002058981,0.00007574978],"domain_scores_gemma":[0.9816206,0.01582567,0.0006881607,0.0006437611,0.001104373,0.0001174433],"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":[0.00004639669,0.00002713011,0.0007842612,0.00003989914,0.00005148552,0.00003190486,0.00002527495,0.9770423,0.0002766506,0.005215977,0.0004975908,0.01596114],"study_design_scores_gemma":[0.000003240437,0.000002316614,0.00002697887,0.000002225568,0.000002052604,0.000002638059,0.000001691273,0.9976212,0.00009109294,0.002146704,0.00009810619,0.000001826254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008314773,0.0001853536,0.9893045,0.0001860608,0.00002586241,0.00005663591,0.0002445252,0.0009372148,0.0007451871],"genre_scores_gemma":[0.295417,0.0004356794,0.6970842,0.0003457993,0.0001557118,0.0006918219,0.001987986,0.0005016923,0.003380064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01759201,"threshold_uncertainty_score":0.03497922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07957458122847148,"score_gpt":0.3827705235691597,"score_spread":0.3031959423406883,"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."}}