{"id":"W4408430395","doi":"10.5194/egusphere-egu25-14261","title":"Bridging the Gap: Integrating Top-Down and Bottom-Up Measurement Approaches to close the Amazon CH4 emissions budget&amp;#160;","year":2025,"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":"ABB (Canada)","funders":"","keywords":"Bridging (networking); Amazon rainforest; Environmental science; Top-down and bottom-up design; Physics; Computer science; Ecology; Biology","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":[0.004200819,0.000957356,0.0008599153,0.002074532,0.0006566735,0.002854873,0.001489198,0.0007315051,0.001125404],"category_scores_gemma":[0.005835785,0.0005537476,0.0006029779,0.002422803,0.0005726069,0.004793993,0.003229227,0.00106723,0.0003195105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001491747,"about_ca_system_score_gemma":0.00255549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03737573,"about_ca_topic_score_gemma":0.07099161,"domain_scores_codex":[0.997818,0.0007389249,0.0001247447,0.0004991652,0.0006090729,0.0002101686],"domain_scores_gemma":[0.9972351,0.0008458392,0.0004004418,0.0004271359,0.0009493027,0.0001423216],"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":[0.000359536,0.000352308,0.224244,0.001121695,0.0006008963,0.0002904966,0.003132383,0.01902989,0.05988922,0.009755667,0.003959136,0.6772648],"study_design_scores_gemma":[0.0001231434,0.0007730509,0.407427,0.001577759,0.0008558852,0.0002935526,0.01205554,0.3808696,0.05090893,0.05185543,0.09283997,0.0004202308],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4408255,0.008591869,0.5047563,0.007686109,0.0004465872,0.0006782433,0.004647762,0.00275808,0.02960963],"genre_scores_gemma":[0.6517447,0.001416037,0.3430614,0.0007560748,0.0001340971,0.0001939684,0.001112856,0.000445271,0.001135615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03737573,"threshold_uncertainty_score":0.07431632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06209696651722205,"score_gpt":0.2414246930126418,"score_spread":0.1793277264954198,"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."}}