{"id":"W2886437980","doi":"10.3390/en11082022","title":"Hydrothermal Carbonization of Fruit Wastes: A Promising Technique for Generating Hydrochar","year":2018,"lang":"en","type":"article","venue":"Energies","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":130,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture, Food and Rural Affairs; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Hydrothermal carbonization; Pomace; Carbonization; Raw material; Heat of combustion; Biomass (ecology); Pulp and paper industry; Thermogravimetric analysis; Carbon fibers; Chemistry; Porosity; Pyrolysis; Valorisation; Waste management; Food science; Materials science; Organic chemistry; Combustion; Adsorption; Agronomy; Composite material","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":[0.00006634626,0.000102692,0.0001212136,0.00005985676,0.0000421578,0.00001318319,0.0001119976,0.00007492932,0.00003593493],"category_scores_gemma":[0.00005160021,0.0001007494,0.00003379484,0.0001345099,0.00005408796,0.00009086425,0.00002360902,0.00003239386,0.000002261279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002464812,"about_ca_system_score_gemma":0.00001458128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005316933,"about_ca_topic_score_gemma":0.000001698168,"domain_scores_codex":[0.9994847,0.000006577717,0.000171673,0.0001155993,0.0000787773,0.000142665],"domain_scores_gemma":[0.9997012,0.00003721034,0.00004076563,0.0001216731,0.00007515803,0.00002399091],"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":[0.000006100816,0.000004871974,0.0000208103,0.0001356419,0.00001585288,1.650236e-7,0.0003551754,0.00263666,0.9956328,0.0001771155,0.00009091678,0.0009238794],"study_design_scores_gemma":[0.0001334147,0.00003515937,0.000002215343,0.00006429273,0.000008292596,0.000001523118,0.00007102476,0.01595291,0.9831921,0.0002109423,0.0002140252,0.0001141328],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9725227,0.000283936,0.0256523,0.00001234903,0.0001088594,0.0002195161,0.00000702843,0.0002703028,0.0009230521],"genre_scores_gemma":[0.9842502,0.000005218794,0.01538038,0.00001343153,0.0001624034,0.0000836372,0.000007178339,0.00003582749,0.00006166167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01331625,"threshold_uncertainty_score":0.4108441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008058845475908202,"score_gpt":0.2108157129657773,"score_spread":0.2027568674898691,"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."}}