{"id":"W1932186180","doi":"10.1002/sia.5104","title":"Quantitative characterization of chemical degradation of heat‐treated wood surfaces during artificial weathering using XPS","year":2012,"lang":"en","type":"article","venue":"Surface and Interface Analysis","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Natural Resources; University of Alberta; Université du Québec à Chicoutimi","funders":"Alberta Innovates; Fonds Québécois de la Recherche sur la Nature et les Technologies; University of Alberta; Université du Québec à Chicoutimi","keywords":"Weathering; Lignin; Hardwood; X-ray photoelectron spectroscopy; Softwood; Cellulose; Chemistry; Chemical composition; Degradation (telecommunications); Chemical engineering; Materials science; Composite material; Organic chemistry; Botany; Geology","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":[0.00009614875,0.0001542585,0.0003595888,0.0001437277,0.00003342157,0.00001712331,0.00004954241,0.00006448395,0.00004708467],"category_scores_gemma":[0.000008217862,0.0001376165,0.00009215016,0.000509779,0.00004635502,0.0005038066,0.00002273059,0.00005566687,0.000002666957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003279783,"about_ca_system_score_gemma":0.000004301254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001564083,"about_ca_topic_score_gemma":0.00001220308,"domain_scores_codex":[0.9992376,0.00003360751,0.0003221168,0.0001186043,0.0001096137,0.0001784402],"domain_scores_gemma":[0.9996854,0.00002474206,0.0000714737,0.0001159184,0.00005984851,0.00004261607],"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":[0.00004806284,0.00003774025,0.05429681,0.00007041033,0.0008891799,1.108238e-7,0.001192868,0.02215786,0.9212077,0.00001685789,2.255722e-7,0.00008220199],"study_design_scores_gemma":[0.0001017401,0.00003035502,0.007537152,0.00004311386,0.0005961586,5.010461e-7,0.000423806,0.0726434,0.9184976,0.000002244918,0.000002296257,0.0001216922],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976423,0.001153889,0.0009618118,0.00000778638,0.00003953264,0.00007724074,0.00002704052,0.00004410398,0.00004625267],"genre_scores_gemma":[0.9991375,0.0001559065,0.0006131482,5.999474e-7,0.00001072175,0.000001380791,0.00003486879,0.00001744651,0.00002843356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05048554,"threshold_uncertainty_score":0.5611837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02812787863098487,"score_gpt":0.2498271928556771,"score_spread":0.2216993142246922,"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."}}