{"id":"W2995040008","doi":"","title":"Improved spatial prediction: A combinatorial approach","year":2014,"lang":"en","type":"article","venue":"EGU General Assembly Conference Abstracts","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Multivariate interpolation; Copula (linguistics); Interpolation (computer graphics); Grid; Spatial dependence; Mathematics; Spatial correlation; Spatial contextual awareness; Computer science; Mathematical optimization; Econometrics; Statistics; Bilinear interpolation; Artificial intelligence; Geometry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004495109,0.0002071792,0.0002534762,0.00005749972,0.0002527639,0.0001737681,0.0002912364,0.000162347,0.001264476],"category_scores_gemma":[0.0001334326,0.0001626872,0.00007589158,0.0001299786,0.00007459721,0.000255703,0.00001729344,0.0002458565,0.0002045102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005846329,"about_ca_system_score_gemma":0.00007616523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001408299,"about_ca_topic_score_gemma":0.0003016559,"domain_scores_codex":[0.9983232,0.0001418431,0.0003859754,0.0004177735,0.0003107862,0.0004203901],"domain_scores_gemma":[0.9990776,0.0001426524,0.000135619,0.0002636546,0.0001030007,0.0002774649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006751374,0.000869431,0.2342112,0.0001091364,0.0002416978,0.00002246719,0.0008853624,0.3712144,0.01961527,0.0515153,0.003211176,0.3174294],"study_design_scores_gemma":[0.0006883773,0.0003830782,0.6167819,0.000003486384,0.00001714087,0.000004157714,0.00001738497,0.370343,0.000235914,0.007785383,0.003506591,0.0002335573],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8776658,0.00001955972,0.00727689,0.0001969448,0.001315672,0.0002310379,0.00004586867,0.0001197741,0.1131284],"genre_scores_gemma":[0.9956624,0.000004510679,0.002086244,0.0002299329,0.001252117,0.000004881537,0.000359054,0.000004529818,0.0003963208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3825707,"threshold_uncertainty_score":0.9996485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02651661835472273,"score_gpt":0.2209532187372765,"score_spread":0.1944366003825538,"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."}}