{"id":"W2100296257","doi":"10.1139/t04-057","title":"Empirical correlations of compression index for marine clay from regression analysis","year":2004,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Agriculture, Soil, Plant Science","field":"Agricultural and Biological Sciences","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Consolidation (business); Void ratio; Atterberg limits; Geotechnical engineering; Regression analysis; Linear regression; Index (typography); Compression (physics); Soil science; Geology; Settlement (finance); Simple linear regression; Statistics; Regression; Environmental science; Mathematics; Water content; Computer science; Materials science; Accounting","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003073419,0.0007196938,0.0004319727,0.00169626,0.0001340125,0.0005362258,0.000587244,0.0003341155,0.001060578],"category_scores_gemma":[0.01582712,0.0002335385,0.0005496712,0.001427206,0.0003690246,0.0007592096,0.0004983218,0.000587042,0.000429904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004983425,"about_ca_system_score_gemma":0.0003800945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002575238,"about_ca_topic_score_gemma":0.003495668,"domain_scores_codex":[0.9986897,0.0004535144,0.0001088191,0.0003013703,0.0003733943,0.0000732183],"domain_scores_gemma":[0.9909757,0.005593808,0.001344279,0.0008037154,0.00119006,0.00009251948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002304786,0.0001454938,0.6244174,0.0001951787,0.0003710806,0.0003042718,0.000283533,0.2363904,0.01152663,0.002598437,0.0009875967,0.1225495],"study_design_scores_gemma":[0.0000214756,0.0001841372,0.1902918,0.00002795406,0.00009687083,0.000341175,0.0001120867,0.7976857,0.008592551,0.001703652,0.0008847279,0.00005792735],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8786709,0.0002981064,0.1184656,0.00005500443,0.00001168008,0.00006429577,0.0005699159,0.0005064306,0.001357984],"genre_scores_gemma":[0.987336,0.00009978867,0.01154655,0.000007039672,0.000004748793,0.00004000512,0.0005777827,0.00003555632,0.000352545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003073419,"threshold_uncertainty_score":0.01625401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02515590231175784,"score_gpt":0.2524464431283112,"score_spread":0.2272905408165534,"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."}}