{"id":"W7100458752","doi":"","title":"Measurement of Precipitation at AWS in Canada: Configuration, Challenges and Alternative Approaches","year":2015,"lang":"en","type":"article","venue":"","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Precipitation; Quantitative precipitation estimation; Climate change; Quantitative precipitation forecast; Weather modification; Plan (archaeology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001354339,0.0003579164,0.0003946518,0.001051471,0.001388278,0.001302871,0.001523147,0.0004208026,0.0006069448],"category_scores_gemma":[0.002595054,0.0002207135,0.0002346581,0.00297282,0.0005598555,0.00079827,0.0006346043,0.0004431513,0.0001334336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007996595,"about_ca_system_score_gemma":0.0071148,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8838637,"about_ca_topic_score_gemma":0.9347547,"domain_scores_codex":[0.9984038,0.0002099614,0.00007029372,0.0001998498,0.000859016,0.0002570543],"domain_scores_gemma":[0.9981611,0.0001651815,0.0001080783,0.0001274133,0.001302823,0.0001354115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006945889,0.0002044496,0.7766045,0.0002840782,0.0001344301,0.0003199323,0.0009394006,0.02704627,0.0195803,0.001243241,0.001969995,0.1709788],"study_design_scores_gemma":[0.0000716818,0.0002705898,0.8980408,0.00005733598,0.00009516966,0.0001713062,0.00187967,0.08258868,0.01097636,0.0004674735,0.005306326,0.0000746232],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976147,0.0005099465,0.009475677,0.0005273121,0.00003958618,0.000182399,0.001606368,0.0002847309,0.01122698],"genre_scores_gemma":[0.9894673,0.0002114103,0.009022696,0.00004364749,0.00001103743,0.00002599653,0.0005669136,0.00001119381,0.0006397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1161363,"threshold_uncertainty_score":0.2336405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1644470638894112,"score_gpt":0.2204716690486562,"score_spread":0.05602460515924498,"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."}}