{"id":"W2798169388","doi":"","title":"Extensive validation of a regularized neural-network technique for S-band polarimetric radar rainfall estimation","year":2009,"lang":"en","type":"article","venue":"34th Conference on Radar Meteorology (5-9 October 2009)","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Remote sensing; Artificial neural network; Radar; Polarimetry; Early-warning radar; Computer science; Environmental science; Geology; Meteorology; Artificial intelligence; Radar imaging; Geography; Bistatic radar; Telecommunications; Physics","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":[0.004411613,0.000900874,0.0006951115,0.0006522596,0.0004578908,0.0005499597,0.001247573,0.00131505,0.001007218],"category_scores_gemma":[0.009770903,0.0003552874,0.0005436769,0.0005381141,0.0006436976,0.0008258263,0.000929795,0.0009075786,0.0003404991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005278732,"about_ca_system_score_gemma":0.0007675421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0114006,"about_ca_topic_score_gemma":0.0101884,"domain_scores_codex":[0.9989698,0.0005341085,0.00007938319,0.0001930744,0.0001536339,0.00006996915],"domain_scores_gemma":[0.9940711,0.003480147,0.0002722538,0.0007667597,0.001331288,0.00007850118],"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":[0.0003482324,0.0002221747,0.004535151,0.00009068204,0.0001681313,0.00005193189,0.00004550023,0.9149254,0.004874585,0.0005486757,0.0008190539,0.07337054],"study_design_scores_gemma":[0.000009322553,0.00003221329,0.0006472119,0.000003314154,0.000007533975,0.00000565955,0.000003683923,0.9980027,0.001140744,0.00009736109,0.00004718157,0.000002995997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7675653,0.0006897816,0.2271652,0.0002102357,0.000114103,0.00008745535,0.0004234923,0.001722479,0.002021892],"genre_scores_gemma":[0.961388,0.00006875501,0.03710918,0.00003700829,0.00001408749,0.00003823487,0.0006288101,0.0000498632,0.000666086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0114006,"threshold_uncertainty_score":0.02333111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03036988178398137,"score_gpt":0.2615705284000199,"score_spread":0.2312006466160385,"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."}}