{"id":"W4414062670","doi":"10.5194/amt-19-3761-2026","title":"Accounting for spatiotemporally correlated errors in wind speed for remote surveys of methane emissions","year":2025,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"California Sea Grant, University of California, San Diego; Natural Resources Canada; United Nations Environment Programme; Natural Sciences and Engineering Research Council of Canada; Ministry of Environment","keywords":"Wind speed; Terrain; Numerical weather prediction; Copula (linguistics); Roughness length; Wind direction; Wind resource assessment; Global wind patterns; Autocorrelation","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.001526824,0.0005988092,0.0003630434,0.0006020315,0.0004266215,0.0007506919,0.001331939,0.0008663161,0.001028699],"category_scores_gemma":[0.007228957,0.0004260111,0.0008333248,0.0008468005,0.0004434837,0.001196325,0.001107904,0.0009812952,0.0002287796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001560191,"about_ca_system_score_gemma":0.002715433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2623298,"about_ca_topic_score_gemma":0.2877402,"domain_scores_codex":[0.9994066,0.0001847504,0.00003387388,0.0001931916,0.00009965416,0.0000819965],"domain_scores_gemma":[0.9979711,0.0008321299,0.0003925171,0.0004001651,0.0003023001,0.0001018514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002553169,0.00003862446,0.05811187,0.00002966788,0.00009993097,0.0001060037,0.00008651406,0.9246566,0.0006679412,0.00268477,0.000585523,0.01290699],"study_design_scores_gemma":[0.000005263649,0.00001098885,0.008874467,0.00001367587,0.00001192944,0.00002067205,0.00004089656,0.9889539,0.0002769355,0.001252087,0.0005252406,0.00001394185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5401437,0.000430071,0.450949,0.0008190905,0.0001109356,0.0001011926,0.002269134,0.001256141,0.003920641],"genre_scores_gemma":[0.9696639,0.0001183046,0.02839291,0.00006322817,0.00003360138,0.00003830446,0.0007642996,0.00009338745,0.0008320371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2623298,"threshold_uncertainty_score":0.5216057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0213858631607599,"score_gpt":0.2606623615002325,"score_spread":0.2392764983394726,"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."}}