{"id":"W2029106573","doi":"10.1515/hf.2005.055","title":"Wood-water sorption isotherm prediction with artificial neural networks: A preliminary study","year":2005,"lang":"en","type":"article","venue":"Holzforschung","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of Arizona","keywords":"Sorption; Artificial neural network; Softwood; Water content; Moisture; Equilibrium moisture content; Biological system; Environmental science; Process engineering; Pulp and paper industry; Materials science; Computer science; Chemistry; Machine learning; Engineering; Composite material; Organic chemistry; Geotechnical engineering","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":[0.00007990778,0.0001873631,0.0001380332,0.0000661115,0.0001002944,0.00006470393,0.00006848716,0.00006063166,0.00007706085],"category_scores_gemma":[0.000001039201,0.000123025,0.0000370376,0.00008269707,0.0000200494,0.0004066992,0.00001640594,0.0001381347,0.00007488489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005034837,"about_ca_system_score_gemma":0.000002612603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001110321,"about_ca_topic_score_gemma":0.00006593466,"domain_scores_codex":[0.9991864,0.00002616496,0.0001834407,0.0001710398,0.0001418382,0.0002911263],"domain_scores_gemma":[0.9997358,0.000008980864,0.00001586429,0.0001759075,0.00001375248,0.00004968931],"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":[0.0006971199,0.0004900878,0.026484,0.00003220382,0.0003056163,0.00003314002,0.005665998,0.8848932,0.003130551,0.00001544068,0.0007107966,0.0775418],"study_design_scores_gemma":[0.00131572,0.002102687,0.02467073,0.00003679204,0.0002015572,0.00003611105,0.0009068889,0.9575044,0.01189852,0.00002340846,0.0008662135,0.0004369574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964389,0.0003754929,0.001152216,0.00007459331,0.0002093034,0.0004562491,0.000002555134,0.0005401212,0.0007506361],"genre_scores_gemma":[0.9988158,0.000009538474,0.00009348289,0.00002001322,0.0006222635,0.00007676541,0.00002799998,0.00004997689,0.0002841165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07710484,"threshold_uncertainty_score":0.5016813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01178765684293707,"score_gpt":0.1865389140862166,"score_spread":0.1747512572432796,"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."}}