{"id":"W2082608203","doi":"10.1623/hysj.48.1.51.43478","title":"Canadian streamflow trend detection: impacts of serial and cross-correlation","year":2003,"lang":"en","type":"article","venue":"Hydrological Sciences Journal","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":490,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of the Environment, Conservation and Parks; Environment and Climate Change Canada","funders":"","keywords":"Statistical significance; Series (stratigraphy); Correlation; Nonparametric statistics; Environmental science; Autocorrelation; Trend analysis; Geography; Physical geography; Statistics; Climatology; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000938605,0.0000865834,0.0001107907,0.00006196989,0.0007715573,0.00005776739,0.000127476,0.0000754807,0.001654663],"category_scores_gemma":[0.000119244,0.00005737371,0.00003339098,0.0002275462,0.0008353136,0.0003022333,0.00004599927,0.0001389403,0.00002641974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007084221,"about_ca_system_score_gemma":0.0000139894,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0027389,"about_ca_topic_score_gemma":0.02868586,"domain_scores_codex":[0.9990097,0.00009975647,0.0001803463,0.0001881833,0.0001908524,0.0003311994],"domain_scores_gemma":[0.9996183,0.00003904378,0.00008923721,0.000049805,0.000004417056,0.0001991864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001942287,0.00002131175,0.9807917,0.000001132596,0.000009487742,0.00001776885,0.000172431,0.01587529,0.0007907397,0.0002412401,0.0001027175,0.001956733],"study_design_scores_gemma":[0.0007520553,0.001254495,0.9674026,0.000006206026,0.00002951921,0.0003859373,0.0001111916,0.004126455,0.001364272,0.01903904,0.005288122,0.0002400675],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796525,0.00004874211,0.0001461868,0.0003560292,0.0002310461,0.00004917416,0.000001085979,0.000006738671,0.01950849],"genre_scores_gemma":[0.9993995,0.00005360709,0.0002601902,0.0001652633,0.00003002611,0.000001293013,2.173727e-7,0.000001574726,0.00008826228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02594696,"threshold_uncertainty_score":0.999258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01205358181409771,"score_gpt":0.2411570790071984,"score_spread":0.2291034971931007,"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."}}