{"id":"W2084217084","doi":"10.1016/j.biortech.2010.01.089","title":"A high-temperature thermal treatment of wood using a multiscale computational model: Application to wood poles","year":2010,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydro-Québec; Université du Québec à Chicoutimi","funders":"","keywords":"Moisture; Evaporation; Heat transfer; Thermal; Water content; Mass transfer; Mechanics; Diffusion; Materials science; Thermodynamics; Process (computing); Heat equation; Steady state (chemistry); Computer science; Mathematics; Chemistry; Engineering; Composite material; Geotechnical engineering; Physics","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":[0.0001983175,0.0003325345,0.0007215663,0.0002061241,0.0006113595,0.0007076956,0.0007175332,0.001395189,0.001383359],"category_scores_gemma":[0.0005633745,0.0003351676,0.0006128557,0.0003284123,0.0005156944,0.0005075954,0.0003683636,0.0004969024,0.0001184212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005383246,"about_ca_system_score_gemma":0.0007924935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00695641,"about_ca_topic_score_gemma":0.005842851,"domain_scores_codex":[0.9999394,0.00001455116,0.000002594365,0.00001402142,0.00001908636,0.00001023621],"domain_scores_gemma":[0.9998252,0.0001015326,0.00001547029,0.00001870353,0.00002389117,0.00001524358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006905288,0.0001031013,0.0007520705,0.00004770188,0.00001204273,0.0001134186,0.00004209042,0.9802786,0.01201656,0.001825919,0.0002313991,0.004507978],"study_design_scores_gemma":[0.00001050108,0.00002037439,0.000194731,0.000001407925,0.000003681756,0.000008370353,0.000007287145,0.998413,0.001068209,0.0001862849,0.00008257281,0.00000362674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8305388,0.0004137115,0.1560352,0.0005318705,0.00008435035,0.0001061815,0.0001898632,0.0002768475,0.01182325],"genre_scores_gemma":[0.9836389,0.0001312172,0.01478139,0.00003003249,0.00001187103,0.00006867376,0.00004736639,0.00002809488,0.00126249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00695641,"threshold_uncertainty_score":0.01383179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007592112563745363,"score_gpt":0.2123350216160879,"score_spread":0.2047429090523425,"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."}}