{"id":"W7117106166","doi":"10.1186/s13021-025-00381-6","title":"An integrative methodology to estimate high-resolution carbon stock and fluxes: a case study in the old-growth forests of the Chilean Patagonia","year":2025,"lang":"en","type":"article","venue":"Carbon Balance and Management","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Agencia Nacional de Investigación y Desarrollo; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Eddy covariance; Carbon flux; Carbon stock; Carbon sequestration; Carbon offset; Ecosystem; Climate change; Carbon cycle; Carbon sink; Stock (firearms)","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":[0.001503897,0.0007148658,0.0003795302,0.001164095,0.0005365809,0.00111261,0.0009786339,0.0005874318,0.0007677869],"category_scores_gemma":[0.002925629,0.0002052626,0.0003344753,0.002400586,0.0004436486,0.0008008525,0.0008493618,0.0005153423,0.00009887919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009144254,"about_ca_system_score_gemma":0.001017571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03328097,"about_ca_topic_score_gemma":0.04367994,"domain_scores_codex":[0.9996173,0.0001542816,0.00002114445,0.0001107626,0.00007177075,0.00002475073],"domain_scores_gemma":[0.9991021,0.0003513875,0.0001567259,0.0001600955,0.0001914084,0.00003824953],"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":[0.0001741182,0.0004868475,0.2928186,0.0004550666,0.0005882586,0.002194579,0.001923403,0.4183764,0.01551253,0.01476045,0.002481939,0.2502279],"study_design_scores_gemma":[0.00006130178,0.0001189355,0.1081433,0.00009580467,0.0001122063,0.0005793945,0.001470443,0.8643355,0.009883916,0.009584188,0.005516618,0.00009835349],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6948683,0.0004708262,0.2936988,0.0005922367,0.00002138242,0.0003228541,0.001492094,0.000631939,0.007901623],"genre_scores_gemma":[0.8681874,0.0001328895,0.1303153,0.00003658481,0.00001034046,0.0001543095,0.0003808781,0.00004526746,0.0007370444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03328097,"threshold_uncertainty_score":0.06617451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01093525476492257,"score_gpt":0.2776140415871413,"score_spread":0.2666787868222187,"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."}}