{"id":"W2606900308","doi":"10.1007/s00107-017-1183-x","title":"Artificial neural network modeling for predicting elastic strain of white birch disks during drying","year":2017,"lang":"en","type":"article","venue":"European Journal of Wood and Wood Products","topic":"Food Drying and Modeling","field":"Agricultural and Biological Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities","keywords":"Artificial neural network; Composite material; Materials science; Strain (injury); Computer science; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.001404096,0.0001511827,0.0002687249,0.00002181661,0.001139537,0.0001554432,0.000330356,0.00002737151,0.000002395135],"category_scores_gemma":[0.0004829309,0.00007006868,0.0001007745,0.00007214364,0.00005390778,0.0002670849,0.0001506119,0.0002341004,4.089809e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006299533,"about_ca_system_score_gemma":0.00001266503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006202673,"about_ca_topic_score_gemma":0.00001019558,"domain_scores_codex":[0.9985386,0.0001271456,0.0005674193,0.0002459089,0.0001932971,0.0003276007],"domain_scores_gemma":[0.9989088,0.00006427473,0.0005984246,0.00009794817,0.0002189215,0.0001116481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004760072,0.0001096919,0.005228412,0.0001686802,0.0001025991,0.00003979953,0.001695222,0.0872284,0.7593517,0.00004774589,0.00004526895,0.1455064],"study_design_scores_gemma":[0.009592627,0.0207061,0.3036533,0.01140924,0.00180172,0.001738767,0.01181579,0.4507927,0.1753391,0.006168477,0.001675279,0.005306966],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974857,0.0005579392,0.0001743518,0.001024479,0.0004802537,0.0001264782,0.00001660026,0.00001726617,0.0001169362],"genre_scores_gemma":[0.9958324,0.00003586149,0.001006822,0.00001386347,0.003073037,6.664549e-7,0.000003261194,0.000005090034,0.00002895025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5840126,"threshold_uncertainty_score":0.876451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05713161390472088,"score_gpt":0.2429983275864631,"score_spread":0.1858667136817423,"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."}}