{"id":"W4317511244","doi":"10.1029/2022ja030835","title":"m‐NLP Inference Models Using Simulation and Regression Techniques","year":2023,"lang":"en","type":"article","venue":"Journal of Geophysical Research Space Physics","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Compute Canada","keywords":"Inference; Computer science; Regression; Artificial intelligence; Data set; Satellite; Set (abstract data type); 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":[0.00391248,0.001199417,0.001253724,0.001262017,0.0006167588,0.001217028,0.002724729,0.001474154,0.003102563],"category_scores_gemma":[0.01902328,0.0009339552,0.001244337,0.001113404,0.0007091522,0.001617631,0.0009780802,0.00259982,0.0009710218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001073517,"about_ca_system_score_gemma":0.001394044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01866571,"about_ca_topic_score_gemma":0.01469338,"domain_scores_codex":[0.9988497,0.0005441953,0.00008118631,0.0002940607,0.0001650903,0.00006569283],"domain_scores_gemma":[0.986368,0.01151244,0.0005796249,0.0004670137,0.0009754502,0.00009747275],"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":[0.00003965232,0.00002359208,0.0007434668,0.00002995585,0.00004262357,0.00002590821,0.00002031188,0.9788799,0.0002645969,0.003581034,0.0004041435,0.0159449],"study_design_scores_gemma":[0.0000024851,0.000002121763,0.00002486927,0.000001750649,0.000001653775,0.000002157524,0.000001387804,0.9985303,0.00007573362,0.001282658,0.00007342869,0.000001512965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008900948,0.0001564135,0.9888964,0.0001585827,0.00002342825,0.00005313197,0.0002023587,0.0008761322,0.0007326062],"genre_scores_gemma":[0.3307255,0.0003841624,0.6624292,0.0003027061,0.0001362261,0.0006668816,0.001665687,0.0004011034,0.003288644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01866571,"threshold_uncertainty_score":0.03711408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1606791962896135,"score_gpt":0.4652916517223843,"score_spread":0.3046124554327708,"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."}}