{"id":"W1854104345","doi":"10.1029/2001wr001210","title":"Interaction between deterministic trend and autoregressive process","year":2003,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Autoregressive model; Autocorrelation; Series (stratigraphy); Econometrics; Variance (accounting); Residual; Time series; Lag; Trend analysis; Statistics; Mathematics; STAR model; Process (computing); Stochastic process; Computer science; Autoregressive integrated moving average; Algorithm; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00405905,0.0005088494,0.0006564495,0.001134947,0.0003430792,0.001985439,0.0005607097,0.000929694,0.002451765],"category_scores_gemma":[0.0159478,0.0003958092,0.0009706888,0.001612099,0.000743592,0.002072971,0.001271221,0.001229642,0.0004379026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007193763,"about_ca_system_score_gemma":0.0006666528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002072688,"about_ca_topic_score_gemma":0.002261625,"domain_scores_codex":[0.9951406,0.001989776,0.0003212742,0.0008520914,0.001337685,0.0003586664],"domain_scores_gemma":[0.9826427,0.01316976,0.002076559,0.0008299756,0.001107066,0.0001739756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007165325,0.0004779075,0.3143065,0.001113599,0.001858753,0.004232029,0.002374958,0.1297565,0.018344,0.2803008,0.002859133,0.2436593],"study_design_scores_gemma":[0.00008908281,0.0008810247,0.3514315,0.0002213417,0.001623138,0.002272293,0.001188898,0.447046,0.005538182,0.1634307,0.02597955,0.0002983242],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5810385,0.003740431,0.3733222,0.002004035,0.0002919724,0.000181893,0.0006140911,0.0006274511,0.03817945],"genre_scores_gemma":[0.9762079,0.0009369721,0.01732452,0.0001559874,0.0001004841,0.00008386705,0.00019774,0.00003930245,0.004953317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00405905,"threshold_uncertainty_score":0.02146655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03971921542446244,"score_gpt":0.3479751884946733,"score_spread":0.3082559730702109,"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."}}