{"id":"W2894676415","doi":"10.2166/nh.2018.054","title":"Variability of spatial patterns of autocorrelation and heterogeneity embedded in precipitation","year":2018,"lang":"en","type":"article","venue":"Hydrology research","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Science Foundation","keywords":"Spatial analysis; Autocorrelation; Precipitation; Spatial dependence; Spatial variability; Spatial heterogeneity; Variogram; Environmental science; Multivariate interpolation; Spatial correlation; Spatial distribution; Spatial ecology; Interpolation (computer graphics); Common spatial pattern; Climatology; Statistics; Kriging; Meteorology; Mathematics; Geography; Geology; Computer science; Ecology; Bilinear interpolation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002427843,0.00004804906,0.0002459348,0.0003504585,0.00003360354,0.000005284358,0.0001096284,0.0001097769,0.0003612621],"category_scores_gemma":[0.0004741297,0.00005299367,0.0000290188,0.000211079,0.000203482,0.00008193984,0.00008000683,0.000122429,0.0000297111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000021166,"about_ca_system_score_gemma":0.00001399994,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008028619,"about_ca_topic_score_gemma":0.00301,"domain_scores_codex":[0.9989272,0.000182434,0.0004318174,0.0002526492,0.00004755033,0.0001582903],"domain_scores_gemma":[0.9992728,0.0002298583,0.0001413444,0.0002383975,0.00008898421,0.00002861684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006678651,0.00006791435,0.9945159,0.00002536598,0.0000185369,3.050291e-7,0.0005478605,0.0000413874,0.0002763213,0.003671686,0.000004385489,0.000763544],"study_design_scores_gemma":[0.0002891645,0.0002427154,0.9360349,0.000005069953,0.000002612039,4.063974e-7,0.00001404346,0.04677288,0.0006751245,0.01586275,0.00005879151,0.00004156925],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922441,0.00003808062,0.006386934,0.00008979432,0.00005218628,0.000167443,0.000103656,0.00000294674,0.0009148649],"genre_scores_gemma":[0.9997181,0.00002565078,0.0001293036,0.000007510204,0.00003697372,0.0000166766,0.00003874322,0.000003918781,0.00002311344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05848104,"threshold_uncertainty_score":0.998577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09535885378105642,"score_gpt":0.3399723431852894,"score_spread":0.244613489404233,"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."}}