{"id":"W2160664484","doi":"10.1002/hyp.9944","title":"Total least square method applied to rating curves","year":2013,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Institut National de la Recherche Scientifique","funders":"","keywords":"Rating curve; Statistics; Variable (mathematics); Mathematics; Square (algebra); Mean squared error; Ordinary least squares; Data set; Singular value decomposition; Applied mathematics; Algorithm; Computer science; Mathematical analysis; Geometry","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":["insufficient_payload"],"category_scores_codex":[0.0002061094,0.0002322274,0.0004294597,0.00003356626,0.0001308139,0.00005960439,0.000226554,0.000103442,0.002557782],"category_scores_gemma":[0.003793205,0.0001500127,0.00005280007,0.0004491401,0.00007798864,0.00009079238,0.0001492272,0.0002211283,0.0008588847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001431752,"about_ca_system_score_gemma":0.0000237064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002192671,"about_ca_topic_score_gemma":0.000001953133,"domain_scores_codex":[0.998353,0.00006670962,0.0003684636,0.0004372715,0.000330158,0.0004444055],"domain_scores_gemma":[0.99751,0.001799229,0.00008196395,0.0001752678,0.0001621611,0.0002714437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004741875,0.004886318,0.001351497,0.01631521,0.0003649807,0.0001500021,0.002349883,0.0007488579,0.0163879,0.2203856,0.1798575,0.556728],"study_design_scores_gemma":[0.0006243182,0.000967005,0.001886605,0.0002812372,0.00008740331,0.00007144686,0.0002673991,0.006390798,0.004867711,0.981189,0.002356638,0.001010397],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06305402,0.0004670408,0.9017076,0.006756537,0.0001046433,0.001547455,0.00003314109,0.000748547,0.02558104],"genre_scores_gemma":[0.6715651,0.00001417682,0.3240232,0.003110501,0.0001757728,0.0005116449,0.000009184299,0.000026377,0.0005640329],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7608034,"threshold_uncertainty_score":0.9999191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05412603641131356,"score_gpt":0.3375147914144903,"score_spread":0.2833887550031767,"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."}}