{"id":"W1994194133","doi":"10.1139/l03-079","title":"Neural networks for the inversion of soil surface parameters from synthetic aperture radar satellite data","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Synthetic aperture radar; Artificial neural network; Remote sensing; Satellite; Surface roughness; Computer science; Water content; Mean squared error; Data set; Inversion (geology); Data mining; Algorithm; Artificial intelligence; Geology; Mathematics; Engineering; Materials science; Statistics; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.001064635,0.0008072309,0.0004767167,0.0005653937,0.0002929969,0.0006144869,0.0007379333,0.0008007663,0.001430383],"category_scores_gemma":[0.00351876,0.0004126413,0.0004603057,0.0007314107,0.0003187259,0.0008023435,0.0004588398,0.001033294,0.0005377723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007741322,"about_ca_system_score_gemma":0.0006241294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01327891,"about_ca_topic_score_gemma":0.01273652,"domain_scores_codex":[0.9997389,0.00008842652,0.00002722343,0.00004224425,0.00007635181,0.00002689872],"domain_scores_gemma":[0.9990487,0.0006384493,0.0000643195,0.00003915719,0.0001959081,0.00001347899],"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":[0.0001137393,0.00004691923,0.001184289,0.0001285765,0.0001039139,0.00006053039,0.00004216017,0.8151177,0.003086702,0.004496813,0.001105463,0.1745132],"study_design_scores_gemma":[0.000006074868,0.00001431169,0.0002206006,0.000009056276,0.000006121311,0.000006396434,0.000004241589,0.9971682,0.000682713,0.001500108,0.0003769095,0.000005210988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03042766,0.001939554,0.9639508,0.0002574335,0.00009962678,0.00006537031,0.0001745123,0.001312293,0.001772697],"genre_scores_gemma":[0.5365423,0.002036951,0.4529653,0.0001513134,0.0001717327,0.0005752122,0.0007009441,0.0001333729,0.006722851],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01327891,"threshold_uncertainty_score":0.02640325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01183257632430639,"score_gpt":0.1791902524953227,"score_spread":0.1673576761710163,"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."}}