{"id":"W4366249108","doi":"10.1139/cgj-2022-0131","title":"Fast stratification of geological cross-section from CPT results with missing data using multitask and modified Bayesian compressive sensing","year":2023,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Cone penetration test; Depth sounding; Stratification (seeds); Geology; Bayesian probability; Autocorrelation; Missing data; Geotechnical engineering; Data assimilation; Soil science; Remote sensing; Computer science; Meteorology; Mathematics; Statistics; Artificial intelligence; Machine learning; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006787446,0.0006502914,0.0003972602,0.0007065661,0.0002659687,0.0004371048,0.0007397587,0.0005163436,0.0007517518],"category_scores_gemma":[0.003134283,0.0002915913,0.000479205,0.0006733702,0.0004440678,0.0008296105,0.001047926,0.0007629072,0.0002423137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002833201,"about_ca_system_score_gemma":0.0008315578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004898075,"about_ca_topic_score_gemma":0.008562269,"domain_scores_codex":[0.9996527,0.00006893028,0.00002186692,0.00006732498,0.0001497425,0.00003939218],"domain_scores_gemma":[0.9988378,0.0004612372,0.0001865843,0.0001590611,0.0002879557,0.00006744578],"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":[0.0004456767,0.0001892559,0.01667213,0.0003286638,0.00008893605,0.000478342,0.0004329741,0.5491805,0.07869837,0.007601788,0.002253877,0.3436294],"study_design_scores_gemma":[0.000007549371,0.00003273754,0.002279309,0.00001087382,0.000009013322,0.0000765903,0.00003993099,0.989541,0.005613267,0.001812165,0.0005606919,0.00001683631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06215195,0.00008382822,0.9363906,0.0001041682,0.00003076944,0.00003003102,0.0001630447,0.0003095072,0.0007360292],"genre_scores_gemma":[0.6441821,0.0001980169,0.3532698,0.0001058646,0.00004558873,0.00008218856,0.0009343178,0.00007410192,0.001108065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004898075,"threshold_uncertainty_score":0.009739101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0659042466965721,"score_gpt":0.303482901178392,"score_spread":0.2375786544818199,"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."}}