{"id":"W1528667054","doi":"10.1002/hyp.9552","title":"Russian nesting dolls effect – Using wavelet analysis to reveal non‐stationary and nested stationary signals in water yield from catchments on a northern forested landscape","year":2012,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Actua; Western University","funders":"","keywords":"Environmental science; Morlet wavelet; Climate change; Atlantic multidecadal oscillation; Precipitation; Hydrology (agriculture); North Atlantic oscillation; Wavelet; Climatology; Geology; Geography; Wavelet transform; Discrete wavelet transform; Meteorology","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":[],"consensus_categories":[],"category_scores_codex":[0.0003814658,0.0002126425,0.0003152102,0.0001317602,0.0002148311,0.00002240858,0.0001264213,0.00009693119,0.000308174],"category_scores_gemma":[0.0001407192,0.0001353562,0.0000349163,0.0005280595,0.00009345527,0.0002672714,0.0002179101,0.0001205093,0.0001084467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004259589,"about_ca_system_score_gemma":0.000003688461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005259149,"about_ca_topic_score_gemma":0.0004695138,"domain_scores_codex":[0.9985181,0.0001219286,0.0002627672,0.0004140537,0.0002224392,0.0004606916],"domain_scores_gemma":[0.9992631,0.0004245354,0.00006785413,0.0001174014,0.000007567999,0.0001195084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001819072,0.0001218668,0.9411247,0.00002365172,0.0001004473,0.00002692468,0.001438562,0.05568039,0.0009801772,0.000001038329,0.00003270162,0.0002876173],"study_design_scores_gemma":[0.0004107851,0.0002757378,0.9913283,0.00002734795,0.0001967146,0.000002089188,0.00008349357,0.005649754,0.0011196,0.0006121318,0.00004126788,0.0002527806],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977796,0.00004441912,0.0003393016,0.0007032293,0.00002742742,0.0003765317,0.000008276375,0.00003479828,0.0006864314],"genre_scores_gemma":[0.9982489,0.000006407017,0.0004285591,0.001061267,0.00003757389,0.00007042143,0.00008209877,0.000009487238,0.00005532653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05020358,"threshold_uncertainty_score":0.5519667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02056281367914319,"score_gpt":0.2556357851276598,"score_spread":0.2350729714485166,"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."}}