{"id":"W2740877834","doi":"10.1007/s10584-017-2038-5","title":"Forest sector carbon analyses support land management planning and projects: assessing the influence of anthropogenic and natural factors","year":2017,"lang":"en","type":"article","venue":"Climatic Change","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"U.S. Forest Service; National Aeronautics and Space Administration","keywords":"Environmental science; Greenhouse gas; Climate change; Carbon sink; Carbon accounting; Forest management; Atmospheric carbon cycle; Carbon sequestration; Carbon dioxide in Earth's atmosphere; Carbon fibers; Forest inventory; Environmental resource management; Environmental protection; Ecology; Carbon dioxide; Agroforestry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002740947,0.0001289912,0.0001907483,0.00003259933,0.0002829737,0.0001233589,0.0001996678,0.0000324963,0.00001791174],"category_scores_gemma":[0.0000581919,0.000080878,0.00002289234,0.00005480357,0.0003148658,0.0003423669,0.0003135662,0.00007360837,0.000002015778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004074737,"about_ca_system_score_gemma":0.000002513351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001892846,"about_ca_topic_score_gemma":0.0005521434,"domain_scores_codex":[0.9991871,0.00004448001,0.0001662052,0.0002080584,0.0001985515,0.0001956603],"domain_scores_gemma":[0.9992654,0.0001045964,0.0002444247,0.0003396346,0.000004610488,0.0000413746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002790643,0.00001012722,0.9957703,0.0002446935,0.00002291711,0.00001110725,0.002203793,0.0000168565,0.001044531,0.000001392466,0.000004047241,0.0006674692],"study_design_scores_gemma":[0.0001544141,0.00005432394,0.9872333,0.0001643966,0.00005105853,0.000009755327,0.0007652561,0.01128849,0.0001532757,0.00001332253,0.00001219444,0.0001002292],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988142,0.0001745489,0.000003117769,0.0000487559,0.00009462157,0.0004913238,0.000004493186,0.00001361408,0.0003553412],"genre_scores_gemma":[0.9998191,0.00002228482,0.00006129267,0.00002170496,0.00002324738,0.00001957027,0.000002888369,0.00001036082,0.00001955698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01127163,"threshold_uncertainty_score":0.3298109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0733918484902155,"score_gpt":0.3462972084158656,"score_spread":0.2729053599256501,"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."}}