{"id":"W2517911547","doi":"10.1016/b978-0-12-802391-4.00005-7","title":"Technical Issues Related to Characterization, Extraction, Recovery, and Purification of Proteins from Different Waste Sources","year":2016,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Protein Hydrolysis and Bioactive Peptides","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Extraction (chemistry); Characterization (materials science); Chromatography; Waste management; Chemistry; Environmental science; Engineering; Materials science; Nanotechnology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001196164,0.0003210467,0.0003947249,0.00009388807,0.00006610971,0.00002942573,0.0001784502,0.0004859622,0.00006751475],"category_scores_gemma":[0.0000378344,0.0002439298,0.0001411958,0.000009932042,0.0001322576,0.000005736291,0.0001450011,0.0001381568,0.00001372956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002375712,"about_ca_system_score_gemma":0.00003178866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000155149,"about_ca_topic_score_gemma":0.00001585582,"domain_scores_codex":[0.9985294,0.00004165215,0.0004982078,0.0005909645,0.0001890058,0.0001508038],"domain_scores_gemma":[0.9988197,0.00001365869,0.0004289568,0.000498109,0.0001458738,0.0000936965],"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":[0.00006591932,0.00001227645,0.00003230878,0.00001716707,0.00008388379,4.713945e-7,0.00001785118,1.016033e-7,0.6377737,0.0001737849,0.00001096847,0.3618115],"study_design_scores_gemma":[0.0001933199,0.0002905917,0.0007525259,0.0003770115,0.0000805977,0.000004657098,0.000004057493,8.619291e-7,0.5546511,0.002419336,0.4408734,0.0003525028],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.6038472,0.005314328,0.001114485,0.001257008,0.0003745591,0.00433272,0.001322754,0.00008921483,0.3823477],"genre_scores_gemma":[0.1357164,0.001266709,0.0005003325,0.00007487713,0.0004065645,0.0001568231,0.0005547152,0.00007680881,0.8612468],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.478899,"threshold_uncertainty_score":0.9947167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007895675271433417,"score_gpt":0.2323390316777308,"score_spread":0.2244433564062974,"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."}}