{"id":"W2033548285","doi":"10.1002/env.1108","title":"Statistical inference in Lombard's smooth‐change model","year":2011,"lang":"en","type":"article","venue":"Environmetrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Statistics Canada; Université du Québec à Trois-Rivières","funders":"","keywords":"Estimator; Econometrics; Inference; Statistics; Variance (accounting); Mathematics; Statistical inference; Robustness (evolution); Computer science; Economics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005464742,0.0001867476,0.0003061184,0.0002634106,0.00003600763,0.00001488686,0.0002454825,0.0001389067,0.001138035],"category_scores_gemma":[0.00730576,0.0001707967,0.00003614464,0.0004641408,0.000147678,0.00009796419,0.0001290748,0.000315462,0.000157361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006925951,"about_ca_system_score_gemma":0.00002289015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009079395,"about_ca_topic_score_gemma":0.00001281718,"domain_scores_codex":[0.9984775,0.0001224795,0.0003854291,0.0003193983,0.0003095537,0.0003856343],"domain_scores_gemma":[0.9968688,0.002497843,0.00008295298,0.0003726876,0.00001673396,0.0001609264],"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":[0.00001906801,0.0004002208,0.02231589,0.00004889981,0.000006771316,0.00004630451,0.001038239,0.00001268571,0.00004548165,0.8698334,0.0002461305,0.1059869],"study_design_scores_gemma":[0.0002860288,0.0001030494,0.08965244,0.00001954106,0.00002073139,0.000001137393,0.00004273717,0.041281,0.0001804775,0.8679169,0.0002099106,0.0002860272],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03692137,0.00006197107,0.9521883,0.00001784139,0.00007916468,0.0002020302,0.00007800234,0.00004163356,0.01040976],"genre_scores_gemma":[0.4693601,0.00006325762,0.5303283,0.00009011165,0.00002003448,0.00003167681,0.000002332147,0.000018554,0.00008563483],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4324388,"threshold_uncertainty_score":0.9997751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3912939287957271,"score_gpt":0.3893539993085515,"score_spread":0.001939929487175529,"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."}}