{"id":"W2505945127","doi":"","title":"Numerical Prediction of Engineered Wood Flooring Deformation","year":2005,"lang":"en","type":"article","venue":"Wood and Fiber Science (Society of Wood Science and Technology)","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; FPInnovations; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Orthotropic material; Composite material; Materials science; Moisture; Deformation (meteorology); Shrinkage; Composite number; Finite element method; Structural engineering; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002641905,0.0004379688,0.0004289389,0.0003104098,0.0002687938,0.000393082,0.0005107849,0.001027894,0.001194966],"category_scores_gemma":[0.0008063641,0.0002364091,0.000423265,0.0002132396,0.00044546,0.0002797185,0.0003070417,0.0004340475,0.0001964368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005491041,"about_ca_system_score_gemma":0.0005618471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01023211,"about_ca_topic_score_gemma":0.005310305,"domain_scores_codex":[0.999905,0.00002099463,0.000006111301,0.00001879099,0.00003131585,0.00001784318],"domain_scores_gemma":[0.9996182,0.0002238539,0.00004180433,0.00002741015,0.00006648383,0.00002220097],"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":[0.00001285662,0.00001956723,0.0006076893,0.00001249435,0.000003229244,0.00002484331,0.00001411315,0.9949777,0.002304329,0.0002501876,0.00003993557,0.001733139],"study_design_scores_gemma":[0.000001224087,0.000005621014,0.00009974905,8.420018e-7,4.949977e-7,0.000001950441,0.00000233253,0.9994913,0.0003248107,0.00003529727,0.00003519496,0.000001098358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8108263,0.0001737226,0.1797266,0.0001426313,0.00005656744,0.00007933436,0.0002992495,0.0005600346,0.008135503],"genre_scores_gemma":[0.9710966,0.00006205564,0.02682366,0.0000188951,0.000004460959,0.00006876234,0.0001456721,0.00002914315,0.001750687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01023211,"threshold_uncertainty_score":0.02034515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007912569872615882,"score_gpt":0.1884566653506761,"score_spread":0.1805440954780602,"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."}}