{"id":"W4416248488","doi":"10.48550/arxiv.2510.19722","title":"Semi-Implicit Approaches for Large-Scale Bayesian Spatial Interpolation","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Bayesian inference; Inference; Bayesian probability; Gaussian process; Gaussian; Interpolation (computer graphics); Markov chain Monte Carlo; Poisson distribution; Monte Carlo method","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":[0.003148557,0.0008321191,0.001194019,0.001089922,0.0006924578,0.001271359,0.003781697,0.001493601,0.003620533],"category_scores_gemma":[0.01316296,0.001054665,0.001373885,0.001399484,0.001210328,0.001596141,0.002667199,0.002672531,0.0008833033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001328054,"about_ca_system_score_gemma":0.002861636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0240851,"about_ca_topic_score_gemma":0.03306649,"domain_scores_codex":[0.9987375,0.0005354323,0.00007158663,0.0002169582,0.0003591895,0.00007932372],"domain_scores_gemma":[0.9940386,0.004236068,0.0003062467,0.0006758042,0.0005977082,0.000145616],"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":[0.00004720999,0.00004478329,0.001402673,0.0001022124,0.00008546522,0.00005983792,0.0001402883,0.9022333,0.001379863,0.03787343,0.001345531,0.05528544],"study_design_scores_gemma":[0.000004479454,0.000003358721,0.0000513251,0.000004877895,0.000002224633,0.000005773485,0.000006137947,0.9896024,0.0001817668,0.009617678,0.0005160953,0.000003854735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002350576,0.00006323594,0.996691,0.00006252222,0.00001218028,0.00001685538,0.00007153382,0.0004079987,0.0003241234],"genre_scores_gemma":[0.1477782,0.0001953377,0.8488106,0.0001566073,0.00006566346,0.00024339,0.0006002255,0.0004741602,0.001675862],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0240851,"threshold_uncertainty_score":0.04788983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03796172039285617,"score_gpt":0.26129010770363,"score_spread":0.2233283873107738,"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."}}