{"id":"W4411219198","doi":"10.1016/j.conbuildmat.2025.142092","title":"Applicability of magnetic resonance imaging for mass timber moisture research","year":2025,"lang":"en","type":"article","venue":"Construction and Building Materials","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Moisture; Magnetic resonance imaging; Materials science; Resonance (particle physics); Environmental science; Nuclear magnetic resonance; Composite material; Physics; Radiology","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":[0.0008373194,0.0006393438,0.0003675808,0.00118217,0.0002545944,0.0006297082,0.0007255593,0.001019707,0.003547628],"category_scores_gemma":[0.001042809,0.0002548569,0.0001973504,0.0007210047,0.0005912948,0.001028075,0.0005325581,0.0005612497,0.001054987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002716672,"about_ca_system_score_gemma":0.0002691541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005038228,"about_ca_topic_score_gemma":0.0007390931,"domain_scores_codex":[0.9996819,0.0000921282,0.00001161861,0.00008005238,0.0001160628,0.00001823426],"domain_scores_gemma":[0.9993153,0.0003009517,0.00009314809,0.0001169506,0.0001274662,0.00004616027],"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":[0.000137913,0.0001482557,0.004005039,0.0004972775,0.00002859092,0.0004169533,0.00009558871,0.001661414,0.8830561,0.002983162,0.0007249913,0.1062447],"study_design_scores_gemma":[0.00006994002,0.002701622,0.0326993,0.0004676531,0.000195312,0.006544055,0.0006822519,0.02455923,0.8547684,0.01498158,0.06219732,0.0001333872],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3783555,0.07992719,0.4669022,0.00176184,0.0005163222,0.0006039228,0.001084558,0.001471201,0.06937727],"genre_scores_gemma":[0.7530079,0.02502455,0.2139808,0.0005971317,0.0004985355,0.0003073338,0.0003217902,0.0001018538,0.006160118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003547628,"threshold_uncertainty_score":0.01186806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602349681300535,"score_gpt":0.2766072697144713,"score_spread":0.2605837729014659,"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."}}