{"id":"W2574302296","doi":"10.1021/acs.energyfuels.6b02270","title":"Determination of the Synergism/Antagonism Parameters during Co-gasification of Potassium-Rich Biomass with Non-biomass Feedstock","year":2017,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Carbon Management Canada","keywords":"Biomass (ecology); Raw material; Potassium; Chemistry; Coal; Bioenergy; Pulp and paper industry; Carbon fibers; Biofuel; Waste management; Materials science; Agronomy; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00008404646,0.0002044964,0.0002478385,0.0001078887,0.0001657254,0.00004053313,0.0006696708,0.0001366523,0.00001678008],"category_scores_gemma":[0.00004646991,0.0001543885,0.00008265155,0.0001684403,0.0002279324,0.0002404209,0.00007755509,0.0000637518,0.000003278151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006752942,"about_ca_system_score_gemma":0.00003085203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001060035,"about_ca_topic_score_gemma":0.00001389389,"domain_scores_codex":[0.9989005,0.00002267527,0.0003259141,0.0002267439,0.0002992167,0.0002248931],"domain_scores_gemma":[0.998616,0.00006116374,0.0003613992,0.0007914295,0.0001087258,0.00006130304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002824198,0.00002592443,0.001846495,0.0002085049,0.00005006432,0.000002451548,0.0001050033,0.00002171017,0.9956116,0.00008100556,0.00001842198,0.002000561],"study_design_scores_gemma":[0.000479406,0.00003089527,0.0185003,0.00008785343,0.0000419329,0.000007062788,0.0000395113,0.0001909374,0.9802619,0.000112675,0.00006360594,0.0001839458],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970379,0.00007398925,0.001229248,0.00009530217,0.0002214666,0.0001010037,0.00002121942,0.00007164641,0.001148156],"genre_scores_gemma":[0.9990504,0.00002162279,0.0006710761,0.000006334097,0.00002577113,0.00002898085,0.0000115434,0.00003871763,0.0001455483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01665381,"threshold_uncertainty_score":0.6295779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00865476360259022,"score_gpt":0.2129909659652853,"score_spread":0.2043362023626951,"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."}}