{"id":"W2025406848","doi":"10.1139/t06-029","title":"Pullout capacity of small ground anchors by direct cone penetration test methods and neural networks","year":2006,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Geotechnical Engineering and Underground Structures","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Adelaide","keywords":"Pile; Penetration test; Cone penetration test; Artificial neural network; Structural engineering; Engineering; Geotechnical engineering; Test data; Bearing capacity; Penetration (warfare); Computer science; Artificial intelligence; Subgrade","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003869998,0.0002232144,0.0003173187,0.0001600325,0.0001311574,0.0001052937,0.0001911877,0.0003257891,0.00002505157],"category_scores_gemma":[0.000144409,0.000218615,0.0000885486,0.0002261708,0.0001237683,0.00009737202,0.00001365,0.0008549418,3.158486e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001741212,"about_ca_system_score_gemma":0.00003909584,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01177395,"about_ca_topic_score_gemma":0.006013783,"domain_scores_codex":[0.9987199,0.0000515719,0.0004392361,0.0001757467,0.0001245182,0.0004890796],"domain_scores_gemma":[0.9990331,0.0001956097,0.00006274423,0.0001750872,0.00005589818,0.0004775508],"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":[0.000003031063,0.00001054605,0.0002304173,0.00002562422,0.00002522226,0.00001770169,0.000007177123,0.9758719,0.01054813,0.0003120645,0.002318718,0.01062945],"study_design_scores_gemma":[0.0003189573,0.0001052227,0.01582966,0.00003638974,0.00004599251,0.0004598503,0.000007921742,0.9726273,0.0007268663,0.001460351,0.008002059,0.0003794523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3542157,0.005242855,0.6376376,0.000384772,0.0007089164,0.0002540347,0.00007703649,0.0003569682,0.001122114],"genre_scores_gemma":[0.994727,0.0001034148,0.004826742,0.00004898228,0.0002015907,0.000004293696,0.00001251323,0.00003430774,0.00004119911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6405112,"threshold_uncertainty_score":0.9948067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009155803590522678,"score_gpt":0.2086856419137761,"score_spread":0.1995298383232535,"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."}}