{"id":"W2048380929","doi":"10.1016/j.biortech.2013.07.001","title":"Effect of ozone pretreatment on hydrogen production from barley straw","year":2013,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Dairy Farmers of Ontario","keywords":"Biohydrogen; Chemistry; Straw; Hydrolysis; Enzymatic hydrolysis; Hydrogen production; Lignocellulosic biomass; Lignin; Fermentation; Food science; Dark fermentation; Reducing sugar; Ozone; Biofuel; Pulp and paper industry; Biomass (ecology); Fermentative hydrogen production; Agronomy; Sugar; Biochemistry; Biotechnology; Organic chemistry; Biology; Catalysis","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":[0.00006381414,0.0001571498,0.0001802753,0.0002202724,0.00003071644,0.000005205274,0.0001304581,0.0002562858,0.0002036805],"category_scores_gemma":[0.00002765365,0.0001239796,0.00004610664,0.0002376018,0.0001066099,0.00003799142,0.00002989073,0.0001475734,0.0003789171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005704853,"about_ca_system_score_gemma":0.000002844366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006342449,"about_ca_topic_score_gemma":0.000001198686,"domain_scores_codex":[0.9992991,0.00002474857,0.0001544116,0.0002503615,0.0001007681,0.0001705729],"domain_scores_gemma":[0.9995012,0.00001531257,0.00003771663,0.0003894915,0.00002075027,0.00003554101],"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":[0.00004477209,0.00008010236,0.006640912,0.000118123,0.0001081083,0.000001856032,0.00006473189,0.0002186047,0.9166535,0.00003260827,0.003008805,0.07302794],"study_design_scores_gemma":[0.0003082311,0.0006288352,0.000919507,0.00002286293,0.00002325354,0.000004160948,0.00003761854,0.0003129614,0.9915217,0.0001008978,0.006005469,0.00011451],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970154,0.0004392649,0.0000164005,0.0006237542,0.0002435673,0.0004881143,0.000007056169,0.0007718537,0.0003946165],"genre_scores_gemma":[0.9995135,0.00004626394,0.0001335275,0.000009083292,0.00008671961,0.00005163586,0.00001488024,0.00002043103,0.0001239348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07486825,"threshold_uncertainty_score":0.505574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003721937668542443,"score_gpt":0.184617351506591,"score_spread":0.1808954138380485,"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."}}