{"id":"W3162741250","doi":"10.1149/ma2021-01541331mtgabs","title":"Estimation of Soil Moisture and Earth Resistivity Using Wenner’s Method and Machine Learning","year":2021,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Silt; Support vector machine; Soil resistivity; Electrical resistivity and conductivity; Soil science; k-nearest neighbors algorithm; Leverage (statistics); Artificial intelligence; Machine learning; Computer science; Environmental science; Geology; Engineering","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":[0.0007882586,0.0001003515,0.0002023762,0.00003363268,0.0001653799,0.00004376203,0.00003465341,0.00006393817,0.00003287537],"category_scores_gemma":[0.001409321,0.0000854323,0.00002691234,0.0001915429,0.00004103698,0.00008836967,0.00001748533,0.0002427147,0.000003225968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001208195,"about_ca_system_score_gemma":0.00002756801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004918869,"about_ca_topic_score_gemma":0.0005449023,"domain_scores_codex":[0.9989526,0.0002914554,0.0001885601,0.0002331533,0.0001543921,0.0001798813],"domain_scores_gemma":[0.9987712,0.0008866115,0.0001295077,0.00006998848,0.00004084171,0.0001018753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00002402116,0.0000191742,0.02906129,0.00009738157,0.00001399734,0.0000203133,0.0001097973,0.5088321,0.01085506,0.000005784804,0.000002762414,0.4509583],"study_design_scores_gemma":[0.0001015677,0.00004291914,0.6504354,0.00004202214,0.00002162622,0.00001846171,0.00001751655,0.3177121,0.03007164,0.001338866,0.0001132057,0.00008471997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930701,0.001104638,0.0009974057,0.0001423631,0.00005138885,0.00003545413,0.000007598707,0.00001987208,0.004571232],"genre_scores_gemma":[0.8994825,0.00002748613,0.1002816,0.00002379184,0.00004383698,1.098453e-7,0.00001483162,0.0000025265,0.0001233053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6213741,"threshold_uncertainty_score":0.7435891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02360298977290862,"score_gpt":0.26433314633295,"score_spread":0.2407301565600414,"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."}}