{"id":"W4405120961","doi":"10.1680/jgeen.24.00258","title":"A methodology for improved predictions of surface ground movements around shafts","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Institution of Civil Engineers - Geotechnical Engineering","topic":"Geotechnical Engineering and Underground Structures","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geomechanica (Canada)","funders":"","keywords":"Benchmark (surveying); Context (archaeology); Human settlement; Excavation; Set (abstract data type); Computer science; Ground movement; Field (mathematics); Empirical research; Surface (topology); Civil engineering; Geology; Marine engineering; Structural engineering; Engineering; Geotechnical engineering; Geodesy; Mathematics; Geometry; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000553733,0.0003639267,0.0006009087,0.0003047868,0.00004917253,0.00002924248,0.0005552016,0.0003702196,0.000006149702],"category_scores_gemma":[0.0005498945,0.0003166768,0.0004012782,0.0008673781,0.0001706575,0.000290644,0.0001081424,0.0005317115,3.60036e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001542509,"about_ca_system_score_gemma":0.000049762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002281889,"about_ca_topic_score_gemma":0.000002423376,"domain_scores_codex":[0.9981169,0.000004493555,0.0008603164,0.0003043984,0.0002970364,0.0004168599],"domain_scores_gemma":[0.9990643,0.0002876058,0.0001082572,0.0002432974,0.0001964465,0.0001001342],"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":[0.00001349742,0.00001911775,0.000002509859,0.001702929,0.0001848478,1.423784e-7,0.00005086113,0.7197774,0.2460905,0.03184548,0.0001118008,0.0002009377],"study_design_scores_gemma":[0.0003717623,0.0001209021,0.0002158059,0.0006429827,0.0001337971,0.0000209013,0.00004057559,0.9201488,0.07248721,0.001992782,0.003528506,0.0002959108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1760607,0.001755883,0.817179,0.0001444014,0.001982324,0.0009007559,0.0001526542,0.001306484,0.0005177978],"genre_scores_gemma":[0.9858684,0.0001216213,0.01372239,0.000005314185,0.0001002308,0.00006013317,0.00000615991,0.00007172006,0.00004406051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8098077,"threshold_uncertainty_score":0.9999285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.015941458349854,"score_gpt":0.2345844525763868,"score_spread":0.2186429942265328,"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."}}