{"id":"W2945251489","doi":"10.1016/j.envpol.2019.05.122","title":"Comparison of different interpolation methods and sequential Gaussian simulation to estimate volumes of soil contaminated by As, Cr, Cu, PCP and dioxins/furans","year":2019,"lang":"en","type":"article","venue":"Environmental Pollution","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Technologique des Résidus Industriels; Université du Québec en Abitibi-Témiscamingue; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Kriging; Interpolation (computer graphics); Pentachlorophenol; Gaussian; Estimator; Contamination; Environmental science; Multivariate interpolation; Distribution (mathematics); Mathematics; Statistics; Bilinear interpolation; Soil science; Mathematical optimization; Computer science; Environmental chemistry; Chemistry; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002435259,0.000589763,0.0006690635,0.0009670786,0.000412756,0.0004532615,0.0009836874,0.0008246016,0.001332513],"category_scores_gemma":[0.008696236,0.0003882803,0.0008452942,0.001232101,0.0002952469,0.0006563713,0.000607154,0.0005484928,0.000182027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005621449,"about_ca_system_score_gemma":0.001489692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03529992,"about_ca_topic_score_gemma":0.02348432,"domain_scores_codex":[0.9991115,0.0004952126,0.00006388534,0.0001331881,0.0001239985,0.00007230753],"domain_scores_gemma":[0.9923897,0.005887513,0.0002690483,0.0003794876,0.0009694918,0.0001047804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001417517,0.0002401995,0.01322245,0.0001897024,0.0002313879,0.00004806955,0.0001679363,0.8891463,0.003325118,0.002310478,0.0004259362,0.08927485],"study_design_scores_gemma":[0.00003135134,0.00006468793,0.001335538,0.000006252486,0.00001985384,0.00001458006,0.00002347838,0.9966461,0.001253562,0.0004403407,0.000153643,0.00001061363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4633974,0.0005276121,0.5328246,0.0001216198,0.00007141181,0.0000955181,0.0003696271,0.001234888,0.001357328],"genre_scores_gemma":[0.8022547,0.0001956555,0.1960994,0.0000377207,0.00001659596,0.00009282897,0.0004873353,0.0001432994,0.0006724249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03529992,"threshold_uncertainty_score":0.07018894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01073059191479817,"score_gpt":0.319761649423157,"score_spread":0.3090310575083588,"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."}}