{"id":"W6999159944","doi":"","title":"CaractÃ©risation diÃ©lectrique du manteau neigeux Ã  l'aide d'un radar UHF","year":2000,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Radar; Space-based radar; Radar imaging; Noise (video)","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":[0.000739296,0.0008764906,0.0004831849,0.001462304,0.001922807,0.002115489,0.001129233,0.001375722,0.1384816],"category_scores_gemma":[0.001176189,0.0003366323,0.0004605119,0.000803451,0.0006332175,0.0007766474,0.001053858,0.001081752,0.03600799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003388817,"about_ca_system_score_gemma":0.003705466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1535817,"about_ca_topic_score_gemma":0.1693701,"domain_scores_codex":[0.999289,0.00005742177,0.00001288888,0.00009086003,0.000428414,0.0001214513],"domain_scores_gemma":[0.999239,0.00009888243,0.00003923897,0.0001168207,0.0004104086,0.00009583941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009260791,0.0001220431,0.00524528,0.0004602042,0.00003143635,0.0005940053,0.0007912393,0.003429569,0.06077315,0.01882914,0.391683,0.517115],"study_design_scores_gemma":[0.00003614782,0.00004907086,0.01139245,0.00003332088,0.00001025797,0.0001964158,0.0001458297,0.002152587,0.01593375,0.0005978913,0.9694309,0.00002142394],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04277672,0.004619974,0.03769526,0.002506366,0.002985375,0.0003822935,0.005813837,0.006917524,0.8963027],"genre_scores_gemma":[0.05987609,0.0009431039,0.0120224,0.000200489,0.000206035,0.00007771183,0.002225016,0.001003082,0.9234461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1535817,"threshold_uncertainty_score":0.4632671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002616855684647038,"score_gpt":0.1298274362709425,"score_spread":0.1272105805862955,"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."}}