{"id":"W4389607254","doi":"10.1002/env.2835","title":"Total least squares bias in climate fingerprinting regressions with heterogeneous noise variances and correlated explanatory variables","year":2023,"lang":"en","type":"article","venue":"Environmetrics","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Spurious relationship; Ordinary least squares; Statistics; Econometrics; Context (archaeology); Mathematics; Omitted-variable bias; Noise (video); Linear regression; Computer science; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0003537454,0.0001910835,0.0002964391,0.0003111995,0.0001473385,0.00003960134,0.0001030382,0.000106564,0.0001083921],"category_scores_gemma":[0.001209202,0.0001445555,0.00003055837,0.001197765,0.0001223427,0.00007600644,0.0001610838,0.0002570027,0.00007331776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000033831,"about_ca_system_score_gemma":0.00001125422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003999906,"about_ca_topic_score_gemma":0.00000641362,"domain_scores_codex":[0.9985377,0.00008975522,0.0003130711,0.0003497066,0.0002936015,0.0004162308],"domain_scores_gemma":[0.9972275,0.002316728,0.0001178356,0.0001960951,0.00001024409,0.0001316345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006812331,0.002217341,0.6320433,0.001396953,0.000417897,0.005584098,0.004403111,0.01778352,0.001386932,0.03866484,0.00297943,0.2924414],"study_design_scores_gemma":[0.00470978,0.001137499,0.6364361,0.001250722,0.0003169494,0.0005338966,0.001821623,0.2918683,0.001301631,0.05516158,0.002916012,0.002545841],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847898,0.0003773007,0.01362413,0.00009501578,0.0001496005,0.0002046773,0.00008197233,0.000185259,0.0004922168],"genre_scores_gemma":[0.9726602,0.000469742,0.02642057,0.00003595056,0.00004624239,0.00003074646,0.00001977415,0.00004556417,0.0002712144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2898955,"threshold_uncertainty_score":0.5894802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05805453952721971,"score_gpt":0.2830585074871756,"score_spread":0.2250039679599559,"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."}}