{"id":"W4410817686","doi":"10.1016/j.coal.2025.104814","title":"Component identification of solid biomass fuels using reflected light microscopy: Interlaboratory study 2","year":2025,"lang":"en","type":"article","venue":"International Journal of Coal Geology","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada","funders":"Uniwersytet Śląski w Katowicach; Narodowa Agencja Wymiany Akademickiej; Narodowe Centrum Nauki; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Microscopy; Component (thermodynamics); Identification (biology); Solid fuel; Biomass (ecology); Materials science; Mineralogy; Environmental science; Analytical Chemistry (journal); Environmental chemistry; Chemistry; Geology; Optics; Biology; Botany; Physics; Combustion","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":[0.0002078874,0.0001103021,0.0002524634,0.0004816163,0.00001728915,0.00001750119,0.0005149831,0.00008296008,0.00006165621],"category_scores_gemma":[0.00009338273,0.000107602,0.00006820004,0.0002136171,0.00006736048,0.0001361436,0.00008519331,0.0001507623,0.000005962474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001393849,"about_ca_system_score_gemma":0.00008571626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006972823,"about_ca_topic_score_gemma":0.000004265017,"domain_scores_codex":[0.9987032,0.00004986913,0.000801998,0.0001025426,0.0002321435,0.0001101824],"domain_scores_gemma":[0.9984753,0.00007661915,0.0003433183,0.0001127646,0.0009553172,0.00003667719],"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":[0.0001476942,0.0001623511,0.003506638,0.0000331626,0.0004840997,0.00002733113,0.0001957844,0.0001763326,0.994579,0.00004627737,0.000331305,0.0003099779],"study_design_scores_gemma":[0.0007331611,0.00007165909,0.003945163,0.00006844674,0.00004636867,0.00003707204,0.000213698,0.0003127961,0.9934458,0.0004724505,0.0005764195,0.00007695438],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900501,0.0004634435,0.006446068,0.0003246013,0.002510504,0.00007642722,0.00001198744,0.00002666568,0.00009023985],"genre_scores_gemma":[0.9995711,0.00002062747,0.0002492357,0.0000408252,0.00007072261,0.000001505263,0.000003619396,0.000009575581,0.00003281137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009521014,"threshold_uncertainty_score":0.4387882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01008981632041563,"score_gpt":0.303845318352114,"score_spread":0.2937555020316984,"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."}}