{"id":"W4388661326","doi":"10.22541/essoar.170000369.94748519/v1","title":"More Frequent Spaceborne Sampling of XCO2 Improves Detectability of Carbon Cycle Seasonal Transitions in Arctic-Boreal Ecosystems","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"California Institute of Technology; Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Arctic; Environmental science; Sampling (signal processing); Carbon flux; Climatology; Satellite; Biosphere; Carbon cycle; Atmospheric sciences; Ecosystem; Boreal; Remote sensing; Flux (metallurgy); The arctic; Oceanography; Computer science; Geology; Detector; Ecology; Physics; Astronomy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005318809,0.000455987,0.0002712042,0.0002734082,0.0003753334,0.0004983603,0.0003504333,0.0003570903,0.001112393],"category_scores_gemma":[0.000782731,0.0001619227,0.0002979249,0.0004797205,0.00031642,0.0009697686,0.0004038564,0.0003170557,0.0001057588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003639126,"about_ca_system_score_gemma":0.0003728147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02160404,"about_ca_topic_score_gemma":0.03503861,"domain_scores_codex":[0.9998926,0.00002217223,0.000003533454,0.00003999934,0.00002089596,0.00002080768],"domain_scores_gemma":[0.9997632,0.00008765212,0.00003790509,0.00004318066,0.00003948289,0.00002866424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001225252,0.0006887315,0.2659472,0.0001424085,0.0002846693,0.000168232,0.0002166391,0.5497807,0.1158864,0.001461796,0.001291991,0.06290609],"study_design_scores_gemma":[0.0001313789,0.0002391466,0.1062336,0.00001047468,0.00005880122,0.00005061514,0.00009549306,0.8690334,0.02251394,0.0007055619,0.0008919763,0.00003557598],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948806,0.00004346858,0.00342746,0.00007231423,0.00000785166,0.000006739261,0.0002314759,0.0001442661,0.00118597],"genre_scores_gemma":[0.9937046,0.00002923333,0.00575706,0.00002486449,0.000005993095,0.000005065014,0.0002694454,0.00003018017,0.0001735027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02160404,"threshold_uncertainty_score":0.04295659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01518364426555895,"score_gpt":0.2392722677124046,"score_spread":0.2240886234468456,"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."}}