{"id":"W2569345207","doi":"10.1016/j.agrformet.2016.12.026","title":"Evaluating an eddy covariance technique to estimate point-source emissions and its potential application to grazing cattle","year":2017,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Odor and Emission Control Technologies","field":"Chemical Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Alberta","funders":"University of Melbourne; Commonwealth Scientific and Industrial Research Organisation","keywords":"Environmental science; Eddy covariance; Atmospheric sciences; Greenhouse gas; Wind speed; Atmospheric instability; Point source; Homogeneity (statistics); Meteorology; Statistics; Mathematics; Ecosystem; Geography; Ecology; Physics","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":[0.0002096889,0.0001751168,0.0002337477,0.00004693783,0.0004871463,0.00007532734,0.0002901038,0.0001840683,0.000006853155],"category_scores_gemma":[0.0006945706,0.0001151869,0.00002934856,0.00006932243,0.00004046246,0.0002304322,0.0003081877,0.0001844768,0.00001224332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001759274,"about_ca_system_score_gemma":0.000005367821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009374813,"about_ca_topic_score_gemma":0.00005784221,"domain_scores_codex":[0.9990003,0.00002055121,0.0001915542,0.0003885372,0.00009535144,0.0003037651],"domain_scores_gemma":[0.999283,0.0000523494,0.00008491895,0.0002739766,0.00008030696,0.0002254177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003092831,0.00001242015,0.000358251,0.00001571773,0.00001081607,0.000001448539,0.00007412364,0.007707757,0.9683446,0.00180124,0.00007062451,0.02157209],"study_design_scores_gemma":[0.002441155,0.001806825,0.3619553,0.0002801346,0.0002937974,0.0007219239,0.0005157831,0.2576942,0.3591975,0.008740402,0.004438113,0.001914907],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9029067,0.0000971491,0.09209902,0.004003295,0.00006713399,0.0004676406,0.000006905312,0.0002534486,0.00009867945],"genre_scores_gemma":[0.9798794,0.000008378379,0.01943365,0.00009274534,0.00009832731,0.0002184082,0.00001267195,0.00001142378,0.0002449381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6091471,"threshold_uncertainty_score":0.4697187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01943101650563278,"score_gpt":0.3171638794099045,"score_spread":0.2977328629042718,"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."}}