{"id":"W2078621841","doi":"10.1002/hyp.6353","title":"Predicting river water temperatures using stochastic models: case study of the Moisie River (Québec, Canada)","year":2006,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":124,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Institut National de la Recherche Scientifique","funders":"","keywords":"Streamflow; Mean squared error; Autoregressive model; Environmental science; Stochastic modelling; Regression analysis; Statistics; Regression; STREAMS; Hydrology (agriculture); Mathematics; Discharge; Econometrics; Drainage basin; Geography; Computer science; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006416647,0.0007346544,0.0003324188,0.0003897174,0.0009339107,0.0008950112,0.001103952,0.0006625159,0.0009070202],"category_scores_gemma":[0.001390712,0.0002387137,0.0004908663,0.0009836133,0.0005048547,0.0002814335,0.0002987771,0.0005343207,0.00007651807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01070855,"about_ca_system_score_gemma":0.007441692,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.967949,"about_ca_topic_score_gemma":0.9521861,"domain_scores_codex":[0.9997374,0.00007748257,0.00001300949,0.00006572401,0.00005224566,0.00005420021],"domain_scores_gemma":[0.9990377,0.0004836177,0.00007471079,0.00003912063,0.0003044332,0.00006043852],"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":[0.00004848687,0.00006470224,0.02022337,0.00003360641,0.00003732534,0.0002073706,0.00006663094,0.9728774,0.0006192884,0.0004967545,0.0005864753,0.004738573],"study_design_scores_gemma":[0.00001345178,0.00003023503,0.008867944,0.00000527956,0.00001012015,0.00001008965,0.00009599576,0.9901676,0.0003026401,0.000109797,0.0003745362,0.00001231056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911465,0.0001145392,0.005732337,0.0002633614,0.000009765457,0.00005560574,0.0007264557,0.0001462218,0.001805156],"genre_scores_gemma":[0.9954365,0.00007815706,0.002866602,0.00001934514,0.00000331219,0.00002659266,0.000398098,0.000008071308,0.001163306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03205103,"threshold_uncertainty_score":0.07769638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01779773570906343,"score_gpt":0.2083303808433574,"score_spread":0.190532645134294,"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."}}