{"id":"W4388858094","doi":"10.15376/biores.19.1.288-305","title":"Artificial neural network modeling to predict the efficiency of phosphoric acid-hydrogen peroxide pretreatment of wheat straw","year":2023,"lang":"en","type":"article","venue":"BioResources","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Department of Science and Technology of Sichuan Province; China Scholarship Council; National Natural Science Foundation of China","keywords":"Hemicellulose; Cellulose; Lignin; Phosphoric acid; Hydrogen peroxide; Biomass (ecology); Straw; Pulp and paper industry; Materials science; Lignocellulosic biomass; Chemical engineering; Chemistry; Nuclear chemistry; Biochemistry; Organic chemistry; Agronomy; Biology; Inorganic chemistry; Engineering","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":[0.0001761111,0.0001196883,0.0001418615,0.00007235682,0.00007635886,0.00001123006,0.0001725248,0.00006020206,0.00002758948],"category_scores_gemma":[0.00001108199,0.00008152051,0.00008062315,0.0005358958,0.00004778995,0.00002845883,0.00005095758,0.00006870656,0.00002358449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002240564,"about_ca_system_score_gemma":0.000007483368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004450712,"about_ca_topic_score_gemma":0.000004311586,"domain_scores_codex":[0.9991333,0.00002171534,0.0002484729,0.000161792,0.0002102604,0.0002244801],"domain_scores_gemma":[0.999653,0.00001773866,0.00003139573,0.0002163632,0.00002614811,0.00005536025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004394051,0.00003023804,0.001179113,0.00009142079,0.00004505656,0.000001181702,0.001297696,0.8427227,0.1498714,0.0000118175,0.001104945,0.003600514],"study_design_scores_gemma":[0.00009200994,0.0001457764,0.0005594747,0.00003141283,0.00003373435,0.000001438743,0.0005866784,0.479375,0.5180636,0.00003126303,0.0009661251,0.0001134647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978121,0.0008823374,0.0002361208,0.0001727114,0.000339214,0.0002562565,0.0000151589,0.0001839203,0.0001021963],"genre_scores_gemma":[0.9995862,0.00006461464,0.00006562903,0.00001222978,0.0001990697,0.000009830334,0.000006893086,0.00001527717,0.00004026516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3681922,"threshold_uncertainty_score":0.332431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01818021433350309,"score_gpt":0.2118753161124338,"score_spread":0.1936951017789307,"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."}}