{"id":"W2626952179","doi":"","title":"Biomass Availability Matrix: Methodology to Define High Level Biomass Availability for Bioenergy Purposes, a Quebec Case Study","year":2014,"lang":"en","type":"article","venue":"World Academy of Science, Engineering and Technology, International Journal of Energy and Power Engineering","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Bioenergy; Biomass (ecology); Environmental science; Matrix (chemical analysis); Renewable energy; Agricultural engineering; Agroforestry; Biofuel; Waste management; Engineering; Agronomy; Ecology; Chemistry; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001417158,0.0002711929,0.0004247634,0.002679108,0.00007407862,0.00005597046,0.0005197072,0.0001465605,0.000008750029],"category_scores_gemma":[0.0004937804,0.0002561571,0.00006628483,0.001234946,0.0002835035,0.0002506043,0.0002058028,0.0001975409,3.298462e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001182761,"about_ca_system_score_gemma":0.00002537659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001490638,"about_ca_topic_score_gemma":0.0002161759,"domain_scores_codex":[0.998301,0.00001977276,0.0006970319,0.0003328022,0.000305233,0.0003441053],"domain_scores_gemma":[0.9990506,0.0002344924,0.0001426735,0.0001556533,0.0002321916,0.0001843729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001694326,0.000436753,0.01418227,0.0005200612,0.001274091,0.0001630052,0.0006260932,0.1956453,0.2468754,0.5096197,0.0009906405,0.02949732],"study_design_scores_gemma":[0.007480935,0.001933008,0.03720141,0.0006629901,0.0003866691,0.003552002,0.001387004,0.283238,0.2715497,0.002923813,0.3869454,0.002739046],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8384511,0.0004476049,0.1588486,0.0008740443,0.001000964,0.0001609553,0.00001916309,0.0001647771,0.00003269821],"genre_scores_gemma":[0.9709067,0.00003841381,0.02881919,0.00002913891,0.00007895064,0.00002327475,0.000001504227,0.00002628278,0.0000765825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5066959,"threshold_uncertainty_score":0.9999891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02248638271981173,"score_gpt":0.2793975168739873,"score_spread":0.2569111341541755,"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."}}