{"id":"W4391643684","doi":"10.1007/s13253-024-00602-4","title":"Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study","year":2024,"lang":"en","type":"article","venue":"Journal of Agricultural Biological and Environmental Statistics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Estimator; Computer science; Smoothing; Covariance; Parametric statistics; Sampling (signal processing); Gaussian; Position (finance); Gaussian process; Artificial intelligence; Data mining; Machine learning; Statistics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0006169204,0.0001459991,0.0002359223,0.00001590576,0.0002107591,0.0000552888,0.00008263721,0.00002755066,0.0001268823],"category_scores_gemma":[0.0002686827,0.00006815831,0.00003707976,0.00005600187,0.0002076727,0.0001100826,0.0002131797,0.0002222151,0.000002428604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003932502,"about_ca_system_score_gemma":0.000002156949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008733899,"about_ca_topic_score_gemma":0.00002703176,"domain_scores_codex":[0.9988825,0.0001823366,0.0004058182,0.0001956647,0.0001405218,0.0001931143],"domain_scores_gemma":[0.997916,0.001780904,0.0001356888,0.00004989827,0.000004280417,0.000113223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001504349,0.0005308007,0.0241815,0.00005518375,0.0002125918,0.0009920844,0.003343507,0.0002994376,0.006530199,0.000815661,0.0004939321,0.9623947],"study_design_scores_gemma":[0.001256701,0.003568766,0.9552034,0.00003603469,0.0003312009,0.003792518,0.01941827,0.006767411,0.0001125156,0.0008305525,0.008355149,0.0003274755],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8267744,0.0003962301,0.1717818,0.00003010672,0.0001194942,0.0002183262,0.0006704293,0.000003872723,0.000005408894],"genre_scores_gemma":[0.9632634,0.0006400352,0.03590899,0.00001369593,0.00006242069,0.00000885038,0.00008953406,0.000005115864,0.000007986275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9620672,"threshold_uncertainty_score":0.2779415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0507796794446163,"score_gpt":0.3055630783981036,"score_spread":0.2547833989534873,"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."}}