{"id":"W4406572129","doi":"10.1016/j.jobab.2025.01.002","title":"High-value utilization of agricultural residues based on component characteristics: Potentiality and challenges","year":2025,"lang":"en","type":"article","venue":"Journal of Bioresources and Bioproducts","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Jiangsu Agricultural Science and Technology Independent Innovation Fund; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Component (thermodynamics); Agriculture; Value (mathematics); Environmental science; Computer science; Biochemical engineering; Engineering; Geography; Physics; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001668345,0.0005043737,0.0006349728,0.003068626,0.0004422792,0.003056279,0.0005206083,0.0006508485,0.001671927],"category_scores_gemma":[0.001462237,0.0002442419,0.0007042767,0.004931207,0.0005390401,0.002791819,0.000578197,0.0006247673,0.0005556939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007308885,"about_ca_system_score_gemma":0.001519162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001756914,"about_ca_topic_score_gemma":0.004940244,"domain_scores_codex":[0.9991125,0.0001475648,0.00006720811,0.0001620572,0.0004251805,0.00008547523],"domain_scores_gemma":[0.9988859,0.000384052,0.0001829708,0.00006318799,0.0004331979,0.00005064834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004103698,0.0002220761,0.03249074,0.01587776,0.0003714931,0.002431988,0.0009179456,0.005431513,0.1557512,0.01553666,0.008150534,0.7624076],"study_design_scores_gemma":[0.000041379,0.0008069096,0.093881,0.005169082,0.001318679,0.004069307,0.008188861,0.01775954,0.3103215,0.0366997,0.5214521,0.0002919177],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4833965,0.4162215,0.04453332,0.006196213,0.0005145689,0.0002059456,0.003825808,0.0003121079,0.04479402],"genre_scores_gemma":[0.6789805,0.2896246,0.0223396,0.0005447247,0.0002462257,0.0001024465,0.002312109,0.0001329736,0.005716844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003068626,"threshold_uncertainty_score":0.008823156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01410148955035423,"score_gpt":0.2053498151046733,"score_spread":0.1912483255543191,"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."}}