{"id":"W2937200386","doi":"10.1007/s00449-019-02109-6","title":"Production and recovery of poly-3-hydroxybutyrate bioplastics using agro-industrial residues of hemp hurd biomass","year":2019,"lang":"en","type":"article","venue":"Bioprocess and Biosystems Engineering","topic":"biodegradable polymer synthesis and properties","field":"Materials Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Faculty of Engineering and Architectural Science, Ryerson University; Connaught Fund; University of Toronto","keywords":"Bioplastic; Xylose; Hydrolysate; Chemistry; Food science; Biomass (ecology); Sugar; Industrial and production engineering; Cupriavidus necator; Polyhydroxyalkanoates; Nitrogen; Hydrolysis; Bioprocess; Lignocellulosic biomass; Ralstonia; Fermentation; Biochemistry; Organic chemistry; Waste management; Biology; Agronomy; Bacteria; Enzyme","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":[0.0002258806,0.0004379404,0.0003209751,0.0001960205,0.0001852155,0.0004200035,0.0002160182,0.0003110956,0.0005135637],"category_scores_gemma":[0.0002399743,0.0001594001,0.0003147082,0.0003449257,0.0001792534,0.0003059058,0.0003002263,0.0005353017,0.0003534753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002261192,"about_ca_system_score_gemma":0.0002576603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007985408,"about_ca_topic_score_gemma":0.001108043,"domain_scores_codex":[0.999821,0.00002168381,0.0000194742,0.00002751603,0.00006290402,0.00004751887],"domain_scores_gemma":[0.9998962,0.00001849519,0.00002542769,0.00001752919,0.00001969903,0.00002272454],"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":[0.00004462591,0.00001576946,0.00008940562,0.00001512275,0.000002799856,0.00005176434,0.000008045778,0.0001080378,0.9988846,0.00001408242,0.000004950916,0.0007606352],"study_design_scores_gemma":[0.000001989703,0.00003960462,0.0004717187,0.000002386784,0.000003038479,0.00002625376,0.000008206241,0.0002575259,0.9990473,0.000005644409,0.0001349997,0.000001231462],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961647,0.0004320028,0.002403992,0.00003885071,0.00001307145,0.00001178211,0.0001381774,0.00002850996,0.0007689823],"genre_scores_gemma":[0.9952017,0.0003404876,0.002293451,0.00001201284,0.000003420018,0.00001040335,0.0002961422,0.0000197871,0.0018225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007985408,"threshold_uncertainty_score":0.001717985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0230633648071642,"score_gpt":0.1989782456773483,"score_spread":0.1759148808701841,"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."}}