{"id":"W4388869890","doi":"10.5194/npg-2023-24","title":"A quest for precipitation attractors in weather radar archives","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Predictability; Precipitation; Radar; Attractor; Fractal; Meteorology; Orographic lift; Quantitative precipitation estimation; Weather radar; Climatology; Environmental science; Geography; Geology; Computer science; Mathematics; Statistics; Mathematical analysis","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":[0.0004968888,0.0001738486,0.0002465105,0.0003760375,0.00005418327,0.00009407717,0.0002298191,0.0001140443,0.0006405322],"category_scores_gemma":[0.0001408893,0.0001469439,0.0001661305,0.0001612281,0.00003091792,0.0001043027,0.00001803021,0.0001904767,0.0001616629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005349252,"about_ca_system_score_gemma":0.00007736578,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001816258,"about_ca_topic_score_gemma":0.02593832,"domain_scores_codex":[0.9986964,0.0001025616,0.0003300862,0.0004047798,0.0002406384,0.0002254995],"domain_scores_gemma":[0.99914,0.0004602669,0.000127631,0.0001787526,0.00002996118,0.00006340746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000672165,0.00003202265,0.9374875,0.0001675535,0.0001172005,0.000002355271,0.001595146,0.01766177,0.00007597665,0.000165415,0.001051245,0.04157655],"study_design_scores_gemma":[0.0003100162,0.00004580577,0.9295828,0.0001317692,0.00005451131,1.340812e-7,0.0004736694,0.03859205,0.00008347425,0.02917164,0.001233229,0.0003208448],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8863944,0.001428888,0.04218378,0.004532805,0.003784165,0.003552061,0.0009482472,0.0007510564,0.05642462],"genre_scores_gemma":[0.9796767,0.000183618,0.01134981,0.00007848638,0.0002273046,0.00003195937,0.002184187,0.00001114489,0.006256737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09328237,"threshold_uncertainty_score":0.9918358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07102291180656443,"score_gpt":0.2769258110400259,"score_spread":0.2059028992334615,"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."}}