{"id":"W4401422110","doi":"10.1007/978-3-031-60255-9_9","title":"Conversion of Residual Biomass to 2D Materials for Energy and Environmental Applications","year":2024,"lang":"en","type":"book-chapter","venue":"Springer proceedings in earth and environmental sciences","topic":"Graphene research and applications","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Biomass (ecology); Residual; Environmental science; Materials science; Process engineering; Biochemical engineering; Engineering; Computer science; Ecology; Algorithm; Biology","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":[0.0004153951,0.0001984286,0.0002252572,0.0002196415,0.0001893718,0.000108525,0.000215106,0.0001074133,0.0002038992],"category_scores_gemma":[0.000004516189,0.0001776835,0.00003186875,0.00005084299,0.000907073,0.0001383159,0.0003191253,0.00005348276,0.00002296788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000374672,"about_ca_system_score_gemma":0.00001786142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004219558,"about_ca_topic_score_gemma":0.00001056107,"domain_scores_codex":[0.9984172,0.00000274302,0.0002726865,0.0006292789,0.0003886074,0.0002894698],"domain_scores_gemma":[0.9996203,0.00003389601,0.00009934816,0.00008254998,0.000004830665,0.000159101],"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":[0.00002177714,0.00002277993,0.0003428899,0.0001508456,0.00000582156,4.728754e-7,0.0001002281,6.897044e-7,0.9635974,0.03423833,0.00010894,0.001409877],"study_design_scores_gemma":[0.0002793688,0.0004173025,0.0013314,0.0001808944,0.00003590569,0.00001196495,0.0005939801,0.00002860201,0.8525041,0.01922985,0.1249341,0.0004524493],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841015,0.001714701,0.00006610236,0.0002343167,0.00007436027,0.001022692,0.001031459,0.00002734929,0.01172751],"genre_scores_gemma":[0.9889861,0.001220598,0.001396953,0.00003438809,0.00008131496,0.0002111982,0.00003065851,0.00002156479,0.008017202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1248252,"threshold_uncertainty_score":0.7245724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01314201586835396,"score_gpt":0.2281817608387664,"score_spread":0.2150397449704125,"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."}}