{"id":"W2070337735","doi":"10.1016/s0022-1694(01)00594-7","title":"Power of the Mann–Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series","year":2002,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":2072,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Statistics; Series (stratigraphy); Sample size determination; Skewness; Negative binomial distribution; Mathematics; Magnitude (astronomy); Monte Carlo method; Rank correlation; Statistical power; Percentile; Geology; Poisson distribution","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2148967,0.003112034,0.004125947,0.01024172,0.002213301,0.006277543,0.004683273,0.004386464,0.006528228],"category_scores_gemma":[0.6213108,0.001945792,0.006701301,0.007131446,0.01262107,0.01597459,0.005678304,0.006708294,0.001883726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001135523,"about_ca_system_score_gemma":0.002922713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001257153,"about_ca_topic_score_gemma":0.0007202554,"domain_scores_codex":[0.8220574,0.1201072,0.01208981,0.01867462,0.02411346,0.002957462],"domain_scores_gemma":[0.08890872,0.8771524,0.01062988,0.01603378,0.00526178,0.00201335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01233491,0.0007214898,0.6646981,0.001629394,0.01610974,0.001966863,0.005576055,0.02941901,0.003934624,0.04186327,0.009069381,0.2126772],"study_design_scores_gemma":[0.002743479,0.009360773,0.5032195,0.00073628,0.005510314,0.003966731,0.004570025,0.2868113,0.007922432,0.1556601,0.01857969,0.0009194898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7781969,0.004475253,0.1852632,0.002891549,0.00143012,0.0008954133,0.002173647,0.001101382,0.02357253],"genre_scores_gemma":[0.9773126,0.0005417826,0.01901339,0.0002433603,0.0004945365,0.0004518889,0.0007554833,0.0003594729,0.0008275724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2148967,"threshold_uncertainty_score":0.9681722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01382803257843311,"score_gpt":0.2423900461355246,"score_spread":0.2285620135570915,"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."}}