{"id":"W2038421396","doi":"10.1007/s00226-007-0155-4","title":"Quantification of urea formaldehyde resin in wood fibers using X-ray photoelectron spectroscopy and confocal laser scanning microscopy","year":2007,"lang":"en","type":"article","venue":"Wood Science and Technology","topic":"Cultural Heritage Materials Analysis","field":"Arts and Humanities","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Intertek (Canada); Université Laval","funders":"","keywords":"X-ray photoelectron spectroscopy; Materials science; Urea-formaldehyde; Analytical Chemistry (journal); Microscopy; Fiber; Confocal laser scanning microscopy; Spectroscopy; Raman spectroscopy; Scanning electron microscope; Nuclear chemistry; Chemistry; Chemical engineering; Composite material; Chromatography; Adhesive; Optics; Biomedical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003912298,0.0005574346,0.0003080207,0.0007131304,0.0005739185,0.0005267625,0.0004511715,0.0004519058,0.002241253],"category_scores_gemma":[0.0003242339,0.0003526745,0.0002731466,0.0004002393,0.0003239966,0.0009597584,0.0002309518,0.0005643424,0.0004702059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003801874,"about_ca_system_score_gemma":0.0002905877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003188037,"about_ca_topic_score_gemma":0.006420213,"domain_scores_codex":[0.9998376,0.0000167445,0.000008212646,0.0000531782,0.00004382704,0.00004036223],"domain_scores_gemma":[0.9997092,0.00007773247,0.00003635016,0.00003080124,0.0001149375,0.00003104358],"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":[0.00004039537,0.0000153281,0.0002420899,0.00003174039,0.000005411405,0.00002620597,0.00003677689,0.00004835089,0.9976883,0.0001349201,0.00002262054,0.001707853],"study_design_scores_gemma":[0.000003288039,0.00006802176,0.006121547,0.000006585458,0.00001458728,0.00009100372,0.00005390954,0.0008939032,0.9915901,0.00007219324,0.001079214,0.000005627707],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8973856,0.003727181,0.09076019,0.0001079295,0.00007059974,0.00009630497,0.0006298604,0.0004031933,0.006819117],"genre_scores_gemma":[0.896332,0.00246195,0.08764334,0.0001114122,0.00003394288,0.0001830281,0.001246881,0.0002118941,0.01177552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003188037,"threshold_uncertainty_score":0.007497728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02246177685621329,"score_gpt":0.2743703253111553,"score_spread":0.251908548454942,"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."}}