{"id":"W2115888124","doi":"10.1111/gcb.12731","title":"Forest ecosystem respiration estimated from eddy covariance and chamber measurements under high turbulence and substantial tree mortality from bark beetles","year":2014,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Forest Service; Commonwealth Scientific and Industrial Research Organisation; McMaster University; National Aeronautics and Space Administration","keywords":"Eddy covariance; Ecosystem respiration; Atmospheric sciences; Environmental science; Ecosystem; Flux (metallurgy); Basal area; Growing season; Respiration; Biomass (ecology); Forest ecology; Soil respiration; Hydrology (agriculture); Ecology; Animal science; Botany; Biology; Chemistry; Physics; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004078304,0.0003509044,0.0002746989,0.0003468533,0.0002827564,0.0005255385,0.0002216502,0.0002391395,0.0003810228],"category_scores_gemma":[0.0006674023,0.0001775855,0.0002201103,0.0003185196,0.0002239948,0.0005261828,0.0002717123,0.0002516269,0.0001297979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005947077,"about_ca_system_score_gemma":0.0002511255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01079547,"about_ca_topic_score_gemma":0.03768267,"domain_scores_codex":[0.9997143,0.00004173553,0.00001999685,0.00009905843,0.00006971413,0.00005516283],"domain_scores_gemma":[0.9994753,0.00009960362,0.0002254802,0.00003882016,0.0000978693,0.00006310455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003013224,0.00006128605,0.9112006,0.00004642608,0.0001358812,0.00005064876,0.0002468728,0.001018055,0.07871401,0.00004728319,0.0001264672,0.008051164],"study_design_scores_gemma":[0.000001550132,0.00005872707,0.9969646,0.000001823561,0.00001281721,0.00003575059,0.00004775638,0.0008274179,0.001974134,0.000009557035,0.0000616426,0.00000410789],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992902,0.00007042399,0.0003378429,0.000002594736,0.000001035062,0.000002959271,0.0001459822,0.000005950494,0.0001428808],"genre_scores_gemma":[0.9984535,0.000047505,0.0007480577,0.000009706183,0.000002417344,0.00000731625,0.000615428,0.000004873436,0.0001112884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01079547,"threshold_uncertainty_score":0.0214653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0576953571051174,"score_gpt":0.2607633447038752,"score_spread":0.2030679875987578,"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."}}