{"id":"W4393218718","doi":"10.1371/journal.pone.0296881","title":"Evaluating spatially enabled machine learning approaches to depth to bedrock mapping, Alberta, Canada","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Energy","funders":"","keywords":"Bedrock; Kriging; Elevation (ballistics); Inverse distance weighting; Digital elevation model; Geostatistics; Terrain; Geology; Feature (linguistics); Geographic information system; Variogram; Hydrogeology; Random forest; Cartography; Artificial intelligence; Spatial variability; Machine learning; Computer science; Multivariate interpolation; Remote sensing; Geomorphology; Statistics; Geography; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003116617,0.0001652359,0.0001881188,0.00005448643,0.000177386,0.00009778757,0.000201265,0.00003262218,0.001044712],"category_scores_gemma":[0.0004906771,0.0001651948,0.00002322361,0.0003726413,0.0000138993,0.00006393549,0.0002967972,0.000195951,0.0006193005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003404397,"about_ca_system_score_gemma":0.00009775466,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7115562,"about_ca_topic_score_gemma":0.8610615,"domain_scores_codex":[0.998201,0.00005388671,0.0002254914,0.0004603754,0.0006530394,0.0004062529],"domain_scores_gemma":[0.9993346,0.0001860615,0.00003432672,0.000185241,0.000009044584,0.0002506675],"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":[0.00008787728,0.0008932663,0.2294115,0.001022683,0.0009311973,0.0002159923,0.01924662,0.3478091,0.1109483,0.001223143,0.01376181,0.2744485],"study_design_scores_gemma":[0.0003072262,0.0003571009,0.036799,0.000574437,0.0001548393,0.000005770563,0.0003076608,0.9194473,0.005735297,0.0003627714,0.03509587,0.0008527342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9581047,0.0000954485,0.00586353,0.002438578,0.00012424,0.0006549519,0.00001724617,0.00009521584,0.03260604],"genre_scores_gemma":[0.9548247,0.000003756596,0.03236878,0.0006203122,0.00008794814,0.00008361685,0.00002862936,0.00003606309,0.01194618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5716382,"threshold_uncertainty_score":0.9998685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1145663253310315,"score_gpt":0.2446201107261426,"score_spread":0.1300537853951111,"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."}}