{"id":"W1496021161","doi":"10.1002/2013jb010364","title":"Constructing empirical resolution diagnostics for kriging and minimum curvature gridding","year":2014,"lang":"en","type":"article","venue":"Journal of Geophysical Research Solid Earth","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto; National Science Council","keywords":"Kriging; Variogram; Interpolation (computer graphics); Curvature; Computer science; Algorithm; Function (biology); Resolution (logic); Image resolution; Mathematical optimization; Inverse; Mathematics; Artificial intelligence; Machine learning; Image (mathematics); Geometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001679145,0.00009768659,0.0002370977,0.00007815474,0.0002984302,0.0001012975,0.0001573067,0.00006262223,0.00003026767],"category_scores_gemma":[0.005069142,0.00008062042,0.00006929973,0.0002244231,0.0003453701,0.0001795181,0.000180726,0.0005615434,0.00001678245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005204535,"about_ca_system_score_gemma":0.00003680725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000046878,"about_ca_topic_score_gemma":0.00002093393,"domain_scores_codex":[0.9982175,0.0001617044,0.0002980298,0.0001848,0.000667797,0.0004702052],"domain_scores_gemma":[0.9966456,0.002705706,0.0001501579,0.0001070798,0.0001309199,0.000260545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000825871,0.0006457045,0.2340336,0.0003627845,0.0002136052,0.0001478685,0.003081267,0.002031074,0.0327255,0.02919197,0.09517612,0.6015647],"study_design_scores_gemma":[0.003839295,0.00374506,0.2798001,0.0007386791,0.000114595,0.0002694728,0.002310013,0.4295605,0.004356612,0.1108135,0.1636247,0.0008274264],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9801208,0.00006160266,0.01612335,0.001179277,0.0002058452,0.0001669098,0.000009747475,0.000007832399,0.002124569],"genre_scores_gemma":[0.9864606,0.00007792623,0.01257633,0.0001001025,0.0006565495,0.000003488924,0.000002168612,0.00001214398,0.0001107179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6007373,"threshold_uncertainty_score":0.6068602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0469986826422561,"score_gpt":0.3694359417431267,"score_spread":0.3224372591008706,"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."}}