{"id":"W2788821408","doi":"10.1038/s41598-018-21559-8","title":"Cooling aerosols and changes in albedo counteract warming from CO2 and black carbon from forest bioenergy in Norway","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Norges Forskningsråd","keywords":"Bioenergy; Environmental science; Greenhouse gas; Global warming; Stove; Biomass (ecology); Climate change; Albedo (alchemy); Atmospheric sciences; Biofuel; Ecology; Geography","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":[0.000416105,0.0002139439,0.0002376011,0.0002028745,0.0002495547,0.0004711567,0.0002081415,0.0001624753,0.0005640531],"category_scores_gemma":[0.0003550319,0.0001093216,0.0002968255,0.00017888,0.0003069224,0.0003593659,0.0002615175,0.0001358589,0.00006745008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008245587,"about_ca_system_score_gemma":0.0005473027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.187139,"about_ca_topic_score_gemma":0.161792,"domain_scores_codex":[0.9998844,0.00001917049,0.000007319967,0.00003008252,0.0000203886,0.00003867269],"domain_scores_gemma":[0.9997643,0.00007260984,0.00005097469,0.00001628339,0.00005810987,0.00003761923],"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":[0.001841375,0.0002375018,0.8037033,0.00027765,0.0002395948,0.00139613,0.000772265,0.07382017,0.09125761,0.001740195,0.001401196,0.02331309],"study_design_scores_gemma":[0.00002691238,0.0001171173,0.9770181,0.00001961803,0.00006691733,0.00006355843,0.0006752997,0.01374434,0.006525409,0.0004517909,0.001275393,0.00001568202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982771,0.0001288818,0.0002252694,0.00002706809,0.000007386678,0.000002871536,0.0002789582,0.000009867373,0.001042585],"genre_scores_gemma":[0.9995647,0.00003828272,0.0001436896,0.000006624924,0.000002085646,0.000001819005,0.0001053905,0.000002632231,0.0001347216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.187139,"threshold_uncertainty_score":0.3720995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008089156137726046,"score_gpt":0.2274928363576129,"score_spread":0.2194036802198868,"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."}}