{"id":"W2784238847","doi":"10.1016/j.tust.2018.01.001","title":"Multi-segment trenchless technology method selection algorithm for buried pipelines","year":2018,"lang":"en","type":"article","venue":"Tunnelling and Underground Space Technology","topic":"Geotechnical Engineering and Underground Structures","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Trenchless technology; Selection (genetic algorithm); Algorithm; Engineering; Pipeline transport; Computer science; Engineering drawing; Artificial intelligence; Mechanical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006962818,0.0009017457,0.001011015,0.00184514,0.0007172966,0.0009672224,0.001413269,0.00113875,0.003717552],"category_scores_gemma":[0.00143157,0.0005195871,0.0008086705,0.001043611,0.0003081917,0.001064612,0.0008443418,0.0006375986,0.001027541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000441012,"about_ca_system_score_gemma":0.0014432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00432291,"about_ca_topic_score_gemma":0.005626332,"domain_scores_codex":[0.9996338,0.00005125592,0.00002031531,0.00009841845,0.0001441088,0.00005216999],"domain_scores_gemma":[0.9993824,0.000219856,0.00005769161,0.00004463188,0.0002566962,0.00003867872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003598191,0.0001314541,0.004341835,0.0001746815,0.000132169,0.0002269801,0.0001413108,0.3753657,0.02069969,0.003645228,0.004375381,0.5904057],"study_design_scores_gemma":[0.00001569294,0.00004955096,0.000620096,0.000007894247,0.00002158679,0.00006406105,0.00002010343,0.9941304,0.003002986,0.0008546792,0.001203652,0.000009173177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0342645,0.0004524459,0.9622397,0.00007994428,0.00004310639,0.00005868571,0.0000895552,0.0007975173,0.001974493],"genre_scores_gemma":[0.381832,0.0004592077,0.6058997,0.00009473946,0.00006589641,0.000187872,0.0006344879,0.0002545872,0.0105715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00432291,"threshold_uncertainty_score":0.01243639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01212176962224062,"score_gpt":0.2587857387941497,"score_spread":0.2466639691719091,"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."}}