{"id":"W4301595180","doi":"10.1038/s41598-022-20971-5","title":"Green extraction of bioactive components from carrot industry waste and evaluation of spent residue as an energy source","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Seed and Plant Biochemistry","field":"Agricultural and Biological Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Residue (chemistry); Extraction (chemistry); Pulp and paper industry; Waste management; Chemistry; Process engineering; Computer science; Food science; Environmental science; Biotechnology; Chromatography; Engineering; Biology; Organic chemistry","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":[0.0002804518,0.0005536062,0.0004234048,0.0005866765,0.0002038658,0.0004137419,0.0001546691,0.0004021953,0.0004400356],"category_scores_gemma":[0.0002053503,0.000160844,0.0006148859,0.0003500204,0.0001712311,0.0004351047,0.0002369719,0.0003314359,0.0001903282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002095791,"about_ca_system_score_gemma":0.0002259118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005431307,"about_ca_topic_score_gemma":0.001594238,"domain_scores_codex":[0.9997575,0.00003845757,0.0000165119,0.000036373,0.0001113044,0.00003982426],"domain_scores_gemma":[0.999909,0.00001684941,0.00002305322,0.000006849219,0.0000310312,0.00001321317],"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":[0.00004229856,0.00001434294,0.0001289183,0.0000690271,0.000005902245,0.0000465658,0.00001010198,0.00008781374,0.9982311,0.00003169104,0.000008695701,0.001323594],"study_design_scores_gemma":[0.000002610697,0.000155611,0.0008394819,0.000008424518,0.00001285962,0.00005534993,0.00001734337,0.0002614327,0.9981678,0.00001978872,0.0004542578,0.000005163717],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840465,0.003607282,0.01030279,0.00005991354,0.000035869,0.00007091241,0.0002547796,0.00004735747,0.001574534],"genre_scores_gemma":[0.9815966,0.002441086,0.01326021,0.00005501271,0.000008962164,0.00006790591,0.0003989821,0.0000285626,0.002142545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005866765,"threshold_uncertainty_score":0.001520634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04112598256509729,"score_gpt":0.2555827110138104,"score_spread":0.2144567284487131,"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."}}