{"id":"W1525739002","doi":"10.1002/jsfa.5920","title":"Screening of agro‐industrial wastes for citric acid bioproduction by <i>Aspergillus niger</i><scp>NRRL</scp> 2001 through solid state fermentation","year":2012,"lang":"en","type":"article","venue":"Journal of the Science of Food and Agriculture","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut de Recherche et de Développement en Agroenvironnement; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Solid-state fermentation; Citric acid; Biomass (ecology); Raw material; Pomace; Pulp and paper industry; Food science; Fermentation; Aspergillus niger; Substrate (aquarium); Chemistry; Waste management; Bioproduction; Biotechnology; Agronomy; Biology; Biochemistry; Engineering","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":[0.0005215543,0.00009494655,0.0001478657,0.00003877955,0.0001114899,0.00001831764,0.0001703065,0.00006920066,5.01769e-7],"category_scores_gemma":[0.0002280683,0.00005124898,0.00008112697,0.00032301,0.0001239229,0.00005201131,0.00003950514,0.00008868285,4.558303e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009082652,"about_ca_system_score_gemma":0.00003260212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003475838,"about_ca_topic_score_gemma":4.702291e-7,"domain_scores_codex":[0.9992049,0.00002165053,0.0002625215,0.0001243439,0.0001942885,0.0001922632],"domain_scores_gemma":[0.9992027,0.000003553167,0.0004113919,0.00008995068,0.0002381731,0.00005428278],"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":[0.00001681028,0.00004815189,0.00007680475,0.00001612413,0.00003038825,1.324656e-8,0.0001500692,0.0002660991,0.9922431,0.000006991591,0.006076335,0.001069167],"study_design_scores_gemma":[0.0003397896,0.0003989975,0.0007488237,0.00003594592,0.00003889025,0.0000569243,0.0003412351,0.000003252421,0.9958789,0.00002134211,0.002095569,0.0000403843],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959588,0.002255234,0.00058019,0.0002114964,0.0008154568,0.000130063,0.00003302593,0.000001780433,0.00001396215],"genre_scores_gemma":[0.9974112,0.0003597293,0.001092926,0.00001462342,0.0008708361,0.00000114345,0.000006318981,0.000003864579,0.0002393628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003980766,"threshold_uncertainty_score":0.2089873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.013801872093708,"score_gpt":0.2305981825583714,"score_spread":0.2167963104646634,"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."}}