{"id":"W4412200863","doi":"10.3390/jmse13071332","title":"Determining Non-Dimensional Group of Parameters Governing the Prediction of Penetration Depth and Holding Capacity of Drag Embedment Anchors Using Linear Regression","year":2025,"lang":"en","type":"article","venue":"Journal of Marine Science and Engineering","topic":"Geotechnical Engineering and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Embedment; Drag; Linear regression; Geotechnical engineering; Penetration depth; Group (periodic table); Parasitic drag; Geology; Drag coefficient; Mathematics; Penetration (warfare); Regression analysis; Regression; Mechanics; Statistics; Physics; Operations research; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008017844,0.0001012589,0.0002439552,0.0003169317,0.00005225022,0.00001440273,0.0001125725,0.00004260842,7.273792e-7],"category_scores_gemma":[0.0001775656,0.00007537191,0.00005374188,0.0005167947,0.00009534296,0.0002350705,0.00008554821,0.0001864364,1.114274e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004903362,"about_ca_system_score_gemma":0.00002741383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002436524,"about_ca_topic_score_gemma":0.000001065795,"domain_scores_codex":[0.9989993,0.000007304385,0.0004478911,0.00008976413,0.00032742,0.0001283068],"domain_scores_gemma":[0.9994859,0.00008649575,0.000163844,0.00009411772,0.0001200451,0.00004956448],"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":[0.000003355388,0.000006810385,0.003548222,0.0001294103,0.00002138775,5.439381e-7,0.00007499382,0.6404465,0.3527318,0.00001051978,0.00000130362,0.003025149],"study_design_scores_gemma":[0.0001886192,0.0000675396,0.053247,0.0005855919,0.00005745003,0.00001755933,0.00007094537,0.9078679,0.03782814,0.00001222158,0.000004749506,0.0000523002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9607146,0.00009410839,0.03896556,0.000009713919,0.0001512469,0.00003701316,0.000001518625,0.000009222021,0.00001705157],"genre_scores_gemma":[0.982857,0.00009209437,0.01702168,0.000002171577,0.00001919931,5.495967e-7,3.302544e-7,0.000005814772,0.00000117126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3149036,"threshold_uncertainty_score":0.3073577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01270983285426199,"score_gpt":0.2199383974893178,"score_spread":0.2072285646350558,"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."}}