{"id":"W4391490841","doi":"10.1002/cjce.25174","title":"Thermodynamic, spectroscopic, and molecular characterization of rice straw biomass for use as biofuel feedstock","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biofuel; Pyrolysis; Biomass (ecology); Thermogravimetry; Raw material; Syngas; Bioenergy; Cellulose; Straw; Pulp and paper industry; Fourier transform infrared spectroscopy; Elemental analysis; Nitrogen; Chemistry; Materials science; Chemical engineering; Waste management; Hydrogen; Agronomy; Organic chemistry; Inorganic chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000849252,0.0001316021,0.0001680281,0.0001503411,0.00001767499,0.00008100579,0.0001767779,0.00008903131,0.00001783875],"category_scores_gemma":[0.00007151505,0.0001074992,0.00007176309,0.0001950587,0.00004878044,0.0001565891,0.000009122809,0.0001662792,0.000001421499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001103625,"about_ca_system_score_gemma":0.000108297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007965804,"about_ca_topic_score_gemma":0.000004621351,"domain_scores_codex":[0.9993727,0.000004009974,0.0002430303,0.00008311975,0.00009846492,0.0001987317],"domain_scores_gemma":[0.9995101,0.00009426607,0.00003829795,0.00009370315,0.00006514765,0.0001985028],"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":[0.000005499733,0.00000157493,0.000007343726,0.0003865723,0.00007793504,0.00001233354,0.00008414952,0.000003742914,0.9986712,0.0004344177,0.00001192344,0.0003033397],"study_design_scores_gemma":[0.000123737,0.00002050783,0.00003987991,0.0001488209,0.00004343526,0.00005266028,0.000004212482,0.006180387,0.9927441,0.0003041879,0.000226252,0.0001117711],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952672,0.0009553279,0.003159895,0.0002270951,0.0002116919,0.00009470402,0.00002972578,0.00003729485,0.00001710335],"genre_scores_gemma":[0.9994423,0.00001065515,0.000411745,0.00001828678,0.00006282737,0.00000304402,0.000006207502,0.00003780315,0.000007089587],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006176644,"threshold_uncertainty_score":0.4383692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005348715767241888,"score_gpt":0.1854785627520057,"score_spread":0.1801298469847639,"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."}}