{"id":"W2487421346","doi":"10.82308/26292","title":"Analyzing trends in temperature, streamflow and precipitation over Southern Ontario and Québec using the discreet wavelet transform","year":2013,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Streamflow; Precipitation; Environmental science; Climatology; Trend analysis; Series (stratigraphy); Wavelet; Discrete wavelet transform; Climate change; Wavelet transform; Time series; Principal component analysis; Statistics; Mathematics; Meteorology; Geography; Geology; Computer science; Drainage basin","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001725925,0.0001654184,0.0001026934,0.0007825721,0.0005109602,0.000596329,0.0002292537,0.0001355382,0.0009638871],"category_scores_gemma":[0.000813193,0.00006283033,0.0001327819,0.002200481,0.000175477,0.0001825343,0.0001430153,0.0001727775,0.00009238024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004792162,"about_ca_system_score_gemma":0.004842569,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9759734,"about_ca_topic_score_gemma":0.9854292,"domain_scores_codex":[0.9999063,0.000005354835,0.000004490834,0.00002062353,0.00004274847,0.00002043101],"domain_scores_gemma":[0.9996401,0.00004858252,0.0000436386,0.00001318744,0.0002325319,0.00002200701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002419982,0.00009179777,0.5941212,0.0001913394,0.0001450826,0.0004819801,0.00193514,0.04594538,0.04844074,0.002523941,0.005969125,0.2999123],"study_design_scores_gemma":[0.000006753736,0.00001713093,0.9282835,0.00001636019,0.00002257058,0.00003588023,0.0007938127,0.06412236,0.002207618,0.0001641581,0.00430891,0.00002105371],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829592,0.0002016654,0.008663306,0.0001524019,0.00001195404,0.00003447402,0.003949139,0.00009308296,0.003934805],"genre_scores_gemma":[0.9881813,0.0002121414,0.007182845,0.00001994461,0.000004165749,0.00002144683,0.001749657,0.00001465442,0.002613834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02402663,"threshold_uncertainty_score":0.04833621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01470459109567971,"score_gpt":0.2157722598270115,"score_spread":0.2010676687313318,"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."}}