{"id":"W4406705883","doi":"10.5194/acp-25-867-2025","title":"The role of OCO-3 XCO <sub>2</sub> retrievals in estimating global terrestrial net ecosystem exchanges","year":2025,"lang":"en","type":"article","venue":"Atmospheric chemistry and physics","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Environmental science; Ecosystem; Terrestrial ecosystem; Net (polyhedron); Primary production; Remote sensing; Atmospheric sciences; Ecology; Geography; Geology; Biology; Mathematics","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":[0.001071425,0.0009122438,0.0003286375,0.0005341605,0.0002803029,0.0006823722,0.0004216092,0.0005048949,0.0005081362],"category_scores_gemma":[0.0014162,0.0001776392,0.0004936201,0.0007221238,0.000266344,0.0009824571,0.0004986647,0.0003370916,0.0001329164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004359106,"about_ca_system_score_gemma":0.0006693817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02618496,"about_ca_topic_score_gemma":0.0269867,"domain_scores_codex":[0.9997478,0.00005382341,0.0000199295,0.00008888751,0.00005808658,0.00003152681],"domain_scores_gemma":[0.999478,0.0001863492,0.00009643938,0.00007828034,0.0001188742,0.00004205501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001718984,0.0003049855,0.4371231,0.0004190526,0.00138663,0.0004102103,0.000193995,0.1930867,0.258644,0.000745919,0.003045122,0.1029212],"study_design_scores_gemma":[0.0001873536,0.0000991131,0.3215015,0.00005555178,0.0002685876,0.00006297922,0.0001059514,0.6229776,0.0521819,0.0004167564,0.002050352,0.00009236648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898503,0.0008222161,0.006251355,0.0001389403,0.00007723125,0.00002929057,0.001042261,0.0003199346,0.001468416],"genre_scores_gemma":[0.992079,0.0001270267,0.006337695,0.00007397703,0.0000239101,0.00001564824,0.001105554,0.00005978412,0.0001775104],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02618496,"threshold_uncertainty_score":0.05206507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002925015187084685,"score_gpt":0.1947657199870016,"score_spread":0.1918407047999169,"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."}}