{"id":"W2061491296","doi":"10.1515/hf.2011.107","title":"Predicting the strength of <i>Populus</i> spp. clones using artificial neural networks and ε-regression support vector machines (ε-rSVM)","year":2011,"lang":"en","type":"article","venue":"Holzforschung","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial neural network; Regression; Support vector machine; Stiffness; Regression analysis; Linear regression; Mathematics; Modular design; Biological system; Computer science; Artificial intelligence; Statistics; Engineering; Structural engineering; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007307892,0.0004174385,0.0002641016,0.000460544,0.0001177673,0.0003281742,0.0003092649,0.0002898625,0.0001435357],"category_scores_gemma":[0.0007524176,0.0001387442,0.0003867733,0.0003103438,0.0001542854,0.0002109006,0.0001718319,0.0002324041,0.00007411298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005298013,"about_ca_system_score_gemma":0.0001927243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007015601,"about_ca_topic_score_gemma":0.009274693,"domain_scores_codex":[0.9998267,0.00003503411,0.00001536196,0.00005778126,0.00004836431,0.00001677345],"domain_scores_gemma":[0.9994324,0.0002408454,0.00009362429,0.00002618153,0.0001683403,0.0000385286],"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":[0.0004663294,0.000249866,0.1811997,0.0001925881,0.0002045416,0.0002447796,0.0001964969,0.2168065,0.5381691,0.0001450656,0.0001146815,0.06201023],"study_design_scores_gemma":[0.00001169505,0.000479604,0.3333459,0.0000149111,0.00008088811,0.00007173797,0.0001539442,0.5362822,0.1291056,0.0001314628,0.0002819325,0.00004008768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968818,0.00002784118,0.002885371,0.000005935563,0.000001327575,0.000007140454,0.00005722491,0.00002450891,0.0001088768],"genre_scores_gemma":[0.9951168,0.00002407402,0.004565928,0.000004578947,8.048477e-7,0.00001355006,0.0001300092,0.000003507671,0.0001407006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007015601,"threshold_uncertainty_score":0.01394957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03383830354381429,"score_gpt":0.2228708969995049,"score_spread":0.1890325934556906,"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."}}