{"id":"W2225307076","doi":"10.1061/(asce)co.1943-7862.0001087","title":"Strategies to Reduce Ground Settlement from Shallow Tunnel Excavation: A Case Study in China","year":2016,"lang":"en","type":"article","venue":"Journal of Construction Engineering and Management","topic":"Geotechnical Engineering and Analysis","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Canadian Natural Resources","funders":"National Natural Science Foundation of China","keywords":"Settlement (finance); Excavation; Range (aeronautics); Geotechnical engineering; Civil engineering; Engineering; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0002048836,0.0001455616,0.0002167264,0.0004058521,0.00002130964,0.00005489881,0.00007715482,0.0000330284,0.00001678362],"category_scores_gemma":[0.000009756336,0.0001160714,0.00004606915,0.000247763,0.000009779288,0.0002042362,0.00002910663,0.0001122115,0.000001605101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009023363,"about_ca_system_score_gemma":0.00000542052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003870906,"about_ca_topic_score_gemma":0.0000178998,"domain_scores_codex":[0.9991419,0.000010225,0.0004083161,0.0001222296,0.0001651621,0.0001522191],"domain_scores_gemma":[0.9996832,0.00002446783,0.00004455373,0.0001244004,0.00002727342,0.00009609918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0000103177,0.00004366327,0.0003227247,0.00006481422,0.0003081043,0.0009961023,0.0003976629,0.9551619,0.002301127,0.0005225104,0.00008511148,0.03978601],"study_design_scores_gemma":[0.02251389,0.002286863,0.4593163,0.004737671,0.002011305,0.01363889,0.07624419,0.3823986,0.001944411,0.002642604,0.02769299,0.004572253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7827647,0.0001045879,0.2164,0.000154119,0.0003239566,0.0001170669,0.000003445842,0.00006923269,0.00006281606],"genre_scores_gemma":[0.9931189,0.0001686002,0.006566842,0.000004083879,0.00009132181,0.00001283465,3.85397e-7,0.00001556621,0.00002139594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5727632,"threshold_uncertainty_score":0.4733253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006679215675915229,"score_gpt":0.2104868451635378,"score_spread":0.2038076294876225,"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."}}