{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102123,0.0003134637,0.0002163338,0.0003618432,0.0001551005,0.000237271,0.0002052414,0.000267617,0.0009782284],"category_scores_gemma":[0.000113506,0.0001258619,0.0002737509,0.0003179086,0.0001854673,0.0003607912,0.0001823817,0.0004255727,0.000308883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001709251,"about_ca_system_score_gemma":0.0001318594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002667857,"about_ca_topic_score_gemma":0.0009677528,"domain_scores_codex":[0.9999156,0.000009989999,0.000004802387,0.00002044032,0.00003529583,0.00001387496],"domain_scores_gemma":[0.9999526,0.00001013842,0.00001225626,0.000007116653,0.000009958273,0.000007896755],"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":[0.00003329075,0.00002173157,0.0003300084,0.0002817056,0.0000167953,0.0001420163,0.00002350453,0.0003101558,0.9871301,0.0002405538,0.00009682633,0.0113732],"study_design_scores_gemma":[0.000006945603,0.0001176224,0.002135585,0.00001245895,0.00001476221,0.0003066609,0.0000217162,0.0009833915,0.9914325,0.0001516241,0.004807032,0.000009565619],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9086348,0.0191444,0.06186886,0.0003355814,0.0001448202,0.0001387087,0.0006716098,0.0002786928,0.008782471],"genre_scores_gemma":[0.9782698,0.004590931,0.01389779,0.00007716243,0.0000363417,0.00003262935,0.0002695335,0.0000392844,0.002786506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009782284,"threshold_uncertainty_score":0.003272533,"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."}}