{"id":"W4406187403","doi":"10.1163/22941932-bja10176","title":"Zooming into refractory timber: enhancing anatomical identification with confocal laser scanning microscopy and fluorescence","year":2025,"lang":"en","type":"article","venue":"IAWA Journal - KU Leuven/IAWA Journal","topic":"Wood and Agarwood Research","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Natural Resources Canada; Environment and Climate Change Canada; Université Laval","keywords":"Autofluorescence; Parenchyma; Optical sectioning; Confocal laser scanning microscopy; Microscopy; Light sheet fluorescence microscopy; Fluorescence microscope; Identification (biology); Confocal microscopy; Confocal; Materials science; Fluorescence; Biomedical engineering; Pathology; Biology; Biophysics; Optics; Botany; Medicine; Cell biology","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":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001742729,0.0004310281,0.0005584506,0.0004929581,0.00163618,0.001530135,0.0007019136,0.0003141981,0.0005416528],"category_scores_gemma":[0.0004063842,0.0003529989,0.0002046407,0.0004108376,0.0003864221,0.0009013794,0.0002059059,0.003591974,0.00003172804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006441263,"about_ca_system_score_gemma":0.001065255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004924055,"about_ca_topic_score_gemma":0.00004230588,"domain_scores_codex":[0.9962012,0.0002030017,0.00118072,0.0005260738,0.0009628899,0.0009261521],"domain_scores_gemma":[0.9973917,0.0003292545,0.0006642882,0.0003685274,0.0005642991,0.0006819222],"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":[0.0004866963,0.0001551675,0.00922625,0.000210815,0.0002940359,0.0006998511,0.0009224207,0.00008205877,0.9638831,0.00005667823,0.004551753,0.01943119],"study_design_scores_gemma":[0.005421106,0.000278893,0.004335484,0.005441066,0.0002754326,0.009336433,0.005439382,0.003023291,0.9405696,0.001486926,0.02331633,0.001076016],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880248,0.002764284,0.004921359,0.00163221,0.0005345116,0.00009401347,0.000006394088,0.00005121831,0.001971266],"genre_scores_gemma":[0.9913232,0.0007417639,0.004128715,0.0001921582,0.001054868,0.00000645841,0.000006657459,0.00006079158,0.002485437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02331344,"threshold_uncertainty_score":0.9998922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007761824814669952,"score_gpt":0.2926363386682969,"score_spread":0.2848745138536269,"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."}}