{"id":"W2023073909","doi":"10.4141/s05-104","title":"Towards optimum sampling for regional-scale N<sub>2</sub>O emission monitoring in Canada","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Soil Science","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Environmental science; Sampling (signal processing); Range (aeronautics); Estimator; Interpolation (computer graphics); Scale (ratio); Sample (material); Extrapolation; Variance (accounting); Event (particle physics); Statistics; Snow; Atmospheric sciences; Meteorology; Climatology; Mathematics; Geography; Computer science; Physics; Cartography; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.004821391,0.0003772527,0.0005874075,0.002014752,0.002020021,0.001816907,0.001787473,0.0004286136,0.001076172],"category_scores_gemma":[0.005801273,0.0004650244,0.0004033539,0.003047218,0.0006391771,0.0007111877,0.001262191,0.000570132,0.0002248589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02479238,"about_ca_system_score_gemma":0.04720804,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9858567,"about_ca_topic_score_gemma":0.9936808,"domain_scores_codex":[0.9969767,0.0005706556,0.0001447083,0.0006458794,0.001173209,0.0004888755],"domain_scores_gemma":[0.9933559,0.0005192046,0.0004339851,0.0001932011,0.005156077,0.0003416809],"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":[0.0005083937,0.000202735,0.344119,0.0008970504,0.000200654,0.0003461183,0.002955638,0.04687019,0.06267887,0.007837025,0.01400546,0.5193788],"study_design_scores_gemma":[0.000127169,0.0003487085,0.7464511,0.0004802778,0.0002002672,0.0002118407,0.005064513,0.1250016,0.02967968,0.00370689,0.08851145,0.0002164734],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6002142,0.006965757,0.3380673,0.004516241,0.0001678814,0.003209895,0.009018554,0.002572781,0.03526746],"genre_scores_gemma":[0.6148249,0.002939638,0.3709406,0.0009005046,0.00003358571,0.0006959331,0.002707039,0.0002419518,0.00671589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02479238,"threshold_uncertainty_score":0.1798822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01069665856186924,"score_gpt":0.2013223987799082,"score_spread":0.190625740218039,"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."}}