{"id":"W4210861468","doi":"10.1007/s00107-022-01794-7","title":"Predicting moisture content in kiln dried timbers using machine learning","year":2022,"lang":"en","type":"article","venue":"European Journal of Wood and Wood Products","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Moisture; Water content; Multilayer perceptron; Kiln; Population; Artificial neural network; Environmental science; Mathematics; Computer science; Machine learning; Materials science; Engineering; Composite material; Geotechnical engineering; Waste management","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":[0.0007553228,0.0001619248,0.0002310212,0.000156867,0.0001808001,0.00005042946,0.0001299497,0.00001138278,0.00002439246],"category_scores_gemma":[0.00006380313,0.0001386196,0.00004476133,0.0001948147,0.00002298197,0.0001392852,0.00008683113,0.0006339063,0.00000201527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006204932,"about_ca_system_score_gemma":0.00002283709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001762532,"about_ca_topic_score_gemma":0.000003701116,"domain_scores_codex":[0.9987569,0.0003069996,0.0003770979,0.0001407534,0.0002056712,0.0002125384],"domain_scores_gemma":[0.9996345,0.0000207551,0.0001340205,0.00009344537,0.00004608024,0.00007116982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001012134,0.0006467674,0.2664194,0.0007236335,0.001650772,0.004894568,0.05163998,0.3693644,0.2492681,0.00008938686,0.002370803,0.05192009],"study_design_scores_gemma":[0.05518704,0.02067297,0.142396,0.004085112,0.002384617,0.01771173,0.04755696,0.1306288,0.2011074,0.0003251554,0.3704223,0.007521925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854084,0.01161138,0.000009342115,0.000354131,0.0004682137,0.0001007104,0.000003943179,0.00005009349,0.001993757],"genre_scores_gemma":[0.9985765,0.0001597815,0.0005825277,0.00003339918,0.0002372047,9.138525e-7,0.000004131556,0.00005031566,0.0003552378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3680515,"threshold_uncertainty_score":0.5652743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02804870859183491,"score_gpt":0.1887243861551756,"score_spread":0.1606756775633407,"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."}}