{"id":"W2086981411","doi":"10.1007/s00226-006-0116-3","title":"A NMR study of water distribution in hardwoods at several equilibrium moisture contents","year":2006,"lang":"en","type":"article","venue":"Wood Science and Technology","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":130,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Bound water; Equilibrium moisture content; Robinia; Softwood; Moisture; Water content; Desorption; Chemistry; Liquid water; Free water; Hardwood; Analytical Chemistry (journal); Materials science; Sorption; Composite material; Botany; Chromatography; Thermodynamics; Organic chemistry; Environmental science; Physics; Adsorption; Environmental engineering","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":[0.00008375619,0.00007969658,0.0001223523,0.0001538395,0.00005124205,0.00001330632,0.0001237623,0.00006273505,0.000003485322],"category_scores_gemma":[0.00000666484,0.0000539862,0.000007730552,0.0003856737,0.0002177151,0.0001217191,0.00009542803,0.00006502664,0.000004440237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006283856,"about_ca_system_score_gemma":0.000005796443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001124825,"about_ca_topic_score_gemma":0.0002548665,"domain_scores_codex":[0.9993814,0.000004938477,0.0001233393,0.0001471665,0.0001193818,0.000223755],"domain_scores_gemma":[0.9998105,0.000003083547,0.00001064518,0.0001240446,0.00003758429,0.00001418416],"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":[0.0000367221,0.0004564155,0.3264287,0.00004117137,0.00003213082,0.00004046362,0.001113699,0.0007880678,0.6671878,0.0007134654,0.0004974525,0.002663929],"study_design_scores_gemma":[0.002656273,0.0009275869,0.08691268,0.00003235137,0.00002797987,0.0000320024,0.001393314,0.004391119,0.9005336,0.001505521,0.00125743,0.0003302052],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989108,0.0002124482,0.000004511832,0.0001655328,0.00005248731,0.0001541037,0.000004998403,0.0001010656,0.0003940617],"genre_scores_gemma":[0.9998361,0.000003950495,0.00001352873,0.000002803347,0.000006156899,0.00001782202,0.000005924053,0.000004023704,0.0001097206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.239516,"threshold_uncertainty_score":0.2201493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008009675483183395,"score_gpt":0.1964882536794592,"score_spread":0.1884785781962758,"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."}}