{"id":"W2011365898","doi":"10.1080/07055900.2001.9649671","title":"Spatial representativeness of a long‐term climate network in Canada","year":2001,"lang":"en","type":"article","venue":"ATMOSPHERE-OCEAN","topic":"Climate variability and models","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Interpolation (computer graphics); Multivariate interpolation; Environmental science; Representativeness heuristic; Precipitation; Meteorology; Spatial correlation; Spatial dependence; Latitude; Range (aeronautics); Climatology; Term (time); Climate change; Geography; Statistics; Computer science; Mathematics; Geology; Geodesy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.001190328,0.0001203544,0.0002091075,0.001420901,0.001289914,0.001218587,0.0007465986,0.0001873916,0.0005890856],"category_scores_gemma":[0.005237917,0.0001441496,0.0001935371,0.003747529,0.0004894331,0.0003481484,0.0007199335,0.0001879724,0.00006301791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01612018,"about_ca_system_score_gemma":0.0121917,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9844308,"about_ca_topic_score_gemma":0.9858885,"domain_scores_codex":[0.9992468,0.00011495,0.00004755749,0.0001721112,0.0002564165,0.00016216],"domain_scores_gemma":[0.9951798,0.0006000413,0.0004858903,0.0002472743,0.003127395,0.0003594976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001182244,0.00001978771,0.9592968,0.00005086867,0.00009111717,0.0001470133,0.00092933,0.01988964,0.0006285665,0.001352652,0.002110175,0.01536576],"study_design_scores_gemma":[0.000006128845,0.00001595379,0.9628857,0.00002121852,0.00002515653,0.00005947148,0.001278658,0.032194,0.0002907884,0.0002218661,0.002983308,0.00001776738],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939842,0.0001696486,0.0008210436,0.0001575122,0.000004304919,0.00002559539,0.003054257,0.00003866193,0.001744928],"genre_scores_gemma":[0.9966324,0.00007172491,0.0006997206,0.00001109537,0.000001332117,0.00001073963,0.002136124,0.000003694288,0.0004332302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01612018,"threshold_uncertainty_score":0.1169606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01318682458946695,"score_gpt":0.2346165880305896,"score_spread":0.2214297634411227,"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."}}