{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001458325,0.0003506601,0.0002101979,0.0002651709,0.0002064453,0.0002520939,0.0001823075,0.0002656666,0.001027155],"category_scores_gemma":[0.0001628215,0.0001853344,0.0002525797,0.0003317034,0.0001858979,0.0002614394,0.0001138103,0.0003103811,0.0001408402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00010773,"about_ca_system_score_gemma":0.00008133887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007908089,"about_ca_topic_score_gemma":0.0009312308,"domain_scores_codex":[0.9998938,0.000009482537,0.00000447925,0.00002305001,0.0000478176,0.00002130218],"domain_scores_gemma":[0.9998945,0.00002112057,0.0000206305,0.00000767602,0.00004530055,0.00001080827],"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":[0.00004156185,0.00000530501,0.0005794168,0.00003587966,0.000005341674,0.00002258838,0.00003362816,0.00007754414,0.9981091,0.00001157144,0.00001160601,0.001066502],"study_design_scores_gemma":[0.000002794068,0.0001675061,0.03997568,0.000004592944,0.00001957408,0.00009678296,0.0001274437,0.0008046689,0.9577345,0.0000330867,0.001024645,0.000008758639],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947441,0.0004578166,0.003837567,0.00001008698,0.00001124291,0.00001436113,0.0002811084,0.00003146298,0.000612296],"genre_scores_gemma":[0.9944555,0.0004415763,0.003098906,0.00002167549,0.00000596923,0.00003987539,0.0005019975,0.00002479946,0.001409742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001027155,"threshold_uncertainty_score":0.003436208,"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."}}