{"id":"W4378229365","doi":"10.3390/f14061090","title":"Can Wood Pellets from Canada’s Boreal Forest Reduce Net Greenhouse Gas Emissions from Energy Generation in the UK?","year":2023,"lang":"en","type":"article","venue":"Forests","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Forest Research Institute; Ministry of Natural Resources and Forestry","funders":"Ontario Ministry of Natural Resources and Forestry; Ministry of Natural Resources","keywords":"Environmental science; Greenhouse gas; Slash (logging); Life-cycle assessment; Climate change; Global warming; Biomass (ecology); Pellets; Forestry; Production (economics); Agronomy; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000658173,0.0001714188,0.0001265076,0.0001100065,0.0000904203,0.00005615757,0.000275526,0.00007600955,0.00008451923],"category_scores_gemma":[0.00002488316,0.0001428629,0.00003424875,0.0003849982,0.00001852271,0.00004838308,0.00005382651,0.00009270859,0.00001528025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001356247,"about_ca_system_score_gemma":0.0001037348,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7299145,"about_ca_topic_score_gemma":0.9984167,"domain_scores_codex":[0.9989089,0.00003923002,0.0002322409,0.0002202518,0.0002906043,0.0003088196],"domain_scores_gemma":[0.9994338,0.00004814727,0.0000306322,0.0003718179,0.00001624715,0.00009933952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000008894578,0.00002726521,0.03712736,0.00001000484,0.00005169747,0.0001462859,0.0008957313,0.07467786,0.0004933677,0.00325537,0.879253,0.004053133],"study_design_scores_gemma":[0.0005618086,0.00002276921,0.6647106,0.00003123512,0.00002393308,0.000001362998,0.0002734271,0.2234065,0.0009326452,0.001846527,0.1078835,0.0003056943],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936202,0.0001208216,0.0004100084,0.001448369,0.0005691312,0.0001805133,0.0002210514,0.0002219023,0.003208069],"genre_scores_gemma":[0.9953784,0.0001227852,0.00006110244,0.0002708862,0.000292949,0.00005691325,0.00339643,0.00003878788,0.0003817734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7713695,"threshold_uncertainty_score":0.5825781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01575113048171876,"score_gpt":0.2112027668922155,"score_spread":0.1954516364104968,"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."}}