{"id":"W104000489","doi":"","title":"Prediction of Internal Bond Strength in Particleboard from Screw Withdrawal Resistance Models","year":2006,"lang":"en","type":"article","venue":"Wood and Fiber Science (Society of Wood Science and Technology)","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Embedment; Enhanced Data Rates for GSM Evolution; Materials science; Bond strength; Face (sociological concept); Composite material; Structural engineering; Engineering; Adhesive","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009181424,0.001061912,0.00041219,0.0006323587,0.0001906942,0.0008412789,0.000781429,0.0004460657,0.0009383167],"category_scores_gemma":[0.002296857,0.0006365342,0.0005478839,0.0003561194,0.000248361,0.0004091366,0.000192076,0.0004485167,0.0003996582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001264647,"about_ca_system_score_gemma":0.0008159077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0583018,"about_ca_topic_score_gemma":0.06454936,"domain_scores_codex":[0.9998019,0.00003788166,0.000009734932,0.00005373082,0.00006855121,0.00002812062],"domain_scores_gemma":[0.9993706,0.0003590959,0.00009102721,0.00004277473,0.0001105341,0.00002595686],"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":[0.00009049813,0.0000689208,0.01615425,0.0000494063,0.00005224988,0.00005338779,0.00003803388,0.9524786,0.01420799,0.0004205735,0.0002639254,0.01612215],"study_design_scores_gemma":[0.000007076344,0.00003702542,0.00713475,0.000003939458,0.00001550331,0.000008444951,0.000006961349,0.9884158,0.004058319,0.0001594213,0.0001434996,0.000009236743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9012353,0.000137337,0.09585764,0.00004090049,0.00001010872,0.00006013972,0.000483538,0.0005679307,0.001607162],"genre_scores_gemma":[0.9879943,0.0001092661,0.01055503,0.000007039658,0.000002544832,0.00003623469,0.0004253581,0.00005391637,0.000816375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0583018,"threshold_uncertainty_score":0.1159249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008710388670125921,"score_gpt":0.1820901205650936,"score_spread":0.1733797318949677,"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."}}