{"id":"W2201611733","doi":"10.1007/978-3-540-72108-6_8","title":"Evaluation of NARAD Precipitation Data for Rainfall Monitoring in Eastern Ontario, Canada","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in geoinformation and cartography","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Precipitation; Environmental science; Rain gauge; Radar; Quantitative precipitation estimation; Meteorology; Flood myth; Precipitation types; Quantitative precipitation forecast; Weather radar; Climatology; Geography; Geology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00209349,0.0001853275,0.0002563548,0.0006337536,0.00005038259,0.00003691625,0.0001708471,0.0001627426,0.0002598168],"category_scores_gemma":[0.0001464592,0.0001791154,0.00005786758,0.0001504391,0.00002894207,0.0003774843,0.00001107582,0.0001872099,0.000001164312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005361371,"about_ca_system_score_gemma":0.0004329262,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4417937,"about_ca_topic_score_gemma":0.9940103,"domain_scores_codex":[0.9980699,0.00003437978,0.0006175917,0.0002174663,0.0008905997,0.0001700487],"domain_scores_gemma":[0.9988807,0.0001988184,0.0003452384,0.0002397364,0.0002866894,0.00004881667],"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":[0.00007435273,0.00000576601,0.3641896,0.0001459141,0.0001073741,7.053054e-7,0.002500226,0.03490923,0.000001797162,0.00005441906,0.00005758065,0.597953],"study_design_scores_gemma":[0.003060434,0.0001844284,0.6976092,0.0008802412,0.0006928847,0.000003685769,0.0003244923,0.2632444,0.00006614631,0.01435295,0.01860203,0.0009790309],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4652489,0.02324137,0.05135658,0.0007005703,0.004584059,0.009264234,0.003026452,0.0001148423,0.442463],"genre_scores_gemma":[0.9939706,0.00009642442,0.001395505,0.00007933805,0.00008694537,0.000005339499,0.004133306,0.00000610129,0.0002264072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.596974,"threshold_uncertainty_score":0.7304114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06666365258261224,"score_gpt":0.2659329678636198,"score_spread":0.1992693152810076,"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."}}