{"id":"W2885487231","doi":"","title":"Toward an Attractor of Radar Precipitation Data","year":2015,"lang":"en","type":"article","venue":"37th Conference on Radar Meteorology","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":"Radar; Precipitation; Attractor; Meteorology; Climatology; Remote sensing; Computer science; Geology; Geography; Mathematics; Telecommunications","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001197898,0.0001535977,0.0003142768,0.0001742157,0.00005226611,0.00003535067,0.0007155503,0.0001035473,0.002080224],"category_scores_gemma":[0.0003521628,0.0001290786,0.00004018096,0.0002133139,0.0001246309,0.0005512202,0.00002250794,0.0001400441,0.000190429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005713076,"about_ca_system_score_gemma":0.0002294838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007950875,"about_ca_topic_score_gemma":0.001869965,"domain_scores_codex":[0.9980479,0.0004324301,0.0003471567,0.0004260381,0.0004966367,0.0002497953],"domain_scores_gemma":[0.9985727,0.0001489985,0.0002194829,0.0006078831,0.0002304711,0.0002204562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00192865,0.0005273641,0.4445788,0.00009367854,0.0005843954,0.0000413016,0.006003289,0.001036484,0.00886208,0.01598234,0.007664243,0.5126973],"study_design_scores_gemma":[0.003575986,0.005853006,0.8493505,0.00006275697,0.0005267173,0.00001960253,0.003684771,0.07683761,0.003732935,0.03482376,0.02026661,0.001265709],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9742534,0.0003669224,0.003938067,0.001476525,0.0008166601,0.0002668432,0.0004694555,0.00008203504,0.0183301],"genre_scores_gemma":[0.9908602,0.00003800747,0.007300537,0.0002338384,0.00009511573,9.653459e-7,0.001372303,0.00000398264,0.00009506837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5114316,"threshold_uncertainty_score":0.998832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2386475109183433,"score_gpt":0.3145134679307113,"score_spread":0.07586595701236806,"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."}}