{"id":"W4241656229","doi":"10.1515/iupac.59.0017","title":"Recommended Methods for Characterization of Agricultural Residues and Feed Products Derived Through Bioconversion","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Bioeconomy and Sustainability Development","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bioconversion; Raw material; Biomass (ecology); Agriculture; Lignocellulosic biomass; Agricultural waste; Crop residue; Biotechnology; Biochemical engineering; Substrate (aquarium); Environmental science; Pulp and paper industry; Waste management; Chemistry; Engineering; Biofuel; Biology; Agronomy; Food science; Ecology; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026674,0.002295141,0.001848878,0.008008197,0.0005768069,0.002234948,0.002548431,0.00213045,0.04218796],"category_scores_gemma":[0.01271352,0.0006882006,0.002281473,0.01145761,0.000392092,0.001684296,0.002076658,0.001550693,0.03462753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001844823,"about_ca_system_score_gemma":0.003918449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01576366,"about_ca_topic_score_gemma":0.02640182,"domain_scores_codex":[0.9970734,0.000435458,0.000870151,0.0006855611,0.0007015637,0.0002338422],"domain_scores_gemma":[0.9941402,0.001925111,0.001185141,0.001002426,0.00151017,0.0002369077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004742612,0.00009111691,0.008057898,0.02257563,0.0004620931,0.0001093553,0.00007042971,0.002016778,0.001499331,0.001655075,0.9337918,0.02919624],"study_design_scores_gemma":[0.0003649138,0.00004430793,0.01478062,0.003198274,0.0002431003,0.0001039585,0.00007636953,0.0005213545,0.001387818,0.002030983,0.9771904,0.00005782112],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001440616,0.0002113436,0.0001240585,0.00003944841,0.000008751291,0.00002638976,0.9988635,0.0001393139,0.0004432023],"genre_scores_gemma":[0.0003996914,0.000267464,0.0006932961,0.00004365755,0.000002913295,0.0001696093,0.9980829,0.00003925919,0.0003013446],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04218796,"threshold_uncertainty_score":0.1411328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02295715230507618,"score_gpt":0.368414260161963,"score_spread":0.3454571078568869,"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."}}