{"id":"W2092445955","doi":"10.1190/geo2010-0378.1","title":"Inversion of low-induction number conductivity meter data to predict seasonal saturation variation","year":2011,"lang":"en","type":"article","venue":"Geophysics","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Loam; Saturation (graph theory); Conductivity; Soil science; Electrical resistivity and conductivity; Water content; Geology; Hydraulic conductivity; Porosity; Inversion (geology); Mineralogy; Hydrology (agriculture); Environmental science; Geotechnical engineering; Soil water; Geomorphology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004289941,0.0002894157,0.0002256034,0.0004868133,0.0001976085,0.0003025748,0.0004332059,0.0003430342,0.0007146714],"category_scores_gemma":[0.001427496,0.000216167,0.0002901459,0.0004205465,0.0002160508,0.000392617,0.0002131353,0.0004193911,0.0002116415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006816438,"about_ca_system_score_gemma":0.0006914756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01316297,"about_ca_topic_score_gemma":0.01456987,"domain_scores_codex":[0.9999073,0.00002550416,0.000005483113,0.0000192762,0.00002204554,0.00002047539],"domain_scores_gemma":[0.9996112,0.0001660029,0.00003881952,0.00003680086,0.0001171395,0.0000300192],"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":[0.0002358035,0.0002386823,0.111642,0.00006228359,0.00004843519,0.0001468996,0.0001760108,0.771177,0.06831897,0.0007552037,0.0008740454,0.04632481],"study_design_scores_gemma":[0.00001094362,0.00001176281,0.009277062,0.000002390379,0.000002683841,0.000005499731,0.00001248288,0.9862978,0.004079413,0.0001628383,0.0001311547,0.000005926907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.960848,0.00002486543,0.03630789,0.00008879528,0.00001527282,0.00001921914,0.0002389119,0.0007789925,0.001678001],"genre_scores_gemma":[0.9943377,0.000006877443,0.005345094,0.000007870265,0.000001874559,0.000005661041,0.0001162623,0.0000211247,0.0001575705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01316297,"threshold_uncertainty_score":0.0261727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06884084588283007,"score_gpt":0.2572075301262568,"score_spread":0.1883666842434267,"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."}}