{"id":"W1551733436","doi":"10.1007/s00107-006-0113-0","title":"Artificial neural network and mathematical modeling comparative analysis of nonisothermal diffusion of moisture in wood","year":2006,"lang":"de","type":"article","venue":"European Journal of Wood and Wood Products","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial neural network; Diffusion; Mathematical model; Data set; Experimental data; Magnitude (astronomy); Biological system; Flux (metallurgy); Computer science; Mathematics; Machine learning; Statistics; Artificial intelligence; Materials science; Thermodynamics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007781692,0.0003391161,0.001098598,0.0003801082,0.00007614704,0.00006378642,0.0001558136,0.00005862044,0.00002276078],"category_scores_gemma":[0.0000301602,0.000262221,0.000163762,0.0006987837,0.0001273241,0.0001879182,0.00007991102,0.0003730811,0.00000368377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001910616,"about_ca_system_score_gemma":0.00002665291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001517498,"about_ca_topic_score_gemma":0.0000166944,"domain_scores_codex":[0.9974242,0.0003962126,0.001308296,0.0002540139,0.0003013681,0.0003159427],"domain_scores_gemma":[0.9989802,0.00007297902,0.0004793287,0.0002046057,0.0001713331,0.00009156387],"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":[0.001305716,0.001258126,0.005719618,0.0009052943,0.004268796,0.0002647656,0.01932592,0.9377209,0.02206099,0.0006269762,0.0002761726,0.006266743],"study_design_scores_gemma":[0.007577704,0.006113346,0.05355557,0.004292254,0.01716525,0.0002048825,0.004242337,0.8704544,0.03150481,0.002157379,0.0005968899,0.002135128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9673916,0.03086517,0.000188913,0.0002155014,0.0001862986,0.0001693858,0.00001617455,0.000009788011,0.0009571299],"genre_scores_gemma":[0.997538,0.0003759528,0.001211217,0.000006293193,0.0007667572,5.335365e-7,0.000009592648,0.0000348565,0.00005680815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06726644,"threshold_uncertainty_score":0.999983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02386515645421151,"score_gpt":0.2176105952494186,"score_spread":0.1937454387952071,"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."}}