{"id":"W3048112832","doi":"10.1061/9780784483190.038","title":"Multi-Criteria Decision Making for Multi-Purpose Utility Tunnel Location Selection","year":2020,"lang":"en","type":"article","venue":"Pipelines 2020","topic":"Underground infrastructure and sustainability","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Analytic hierarchy process; Multiple-criteria decision analysis; Selection (genetic algorithm); Computer science; Site selection; Operations research; Geographic information system; Key (lock); Process (computing); Excavation; Transport engineering; Engineering; Artificial intelligence; Geography; Computer security","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":[0.0001595243,0.0002156129,0.0002305596,0.00003941726,0.0001158447,0.00007543029,0.0001486951,0.0001323759,0.0001199871],"category_scores_gemma":[0.0006602537,0.0002151573,0.00009418961,0.0003751374,0.00002525019,0.0002764317,0.00003926325,0.0001659639,0.00002102933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001094838,"about_ca_system_score_gemma":0.0000392056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001234188,"about_ca_topic_score_gemma":0.00007404807,"domain_scores_codex":[0.9988178,0.00003061163,0.0004004611,0.0003515123,0.0001239958,0.0002756065],"domain_scores_gemma":[0.9992995,0.00009367604,0.00004199849,0.0001689659,0.0002883756,0.0001074954],"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":[0.001949528,0.0004891392,0.03007528,0.005120741,0.0002200188,0.00001513536,0.00825581,0.1600332,0.06372666,0.000188352,0.07372829,0.6561978],"study_design_scores_gemma":[0.0007135492,0.00004335016,0.01425042,0.00002100645,0.00002324082,0.000003241851,0.0002477741,0.9725151,0.001086956,0.000585422,0.01028801,0.0002219143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04986271,0.0002564718,0.9480767,0.0002348299,0.0004890387,0.0005681899,0.00002105209,0.0004478827,0.00004315766],"genre_scores_gemma":[0.8868312,0.00001565645,0.11224,0.0002805768,0.0004905619,0.00004698565,0.00003647552,0.00003842021,0.00002011549],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8369685,"threshold_uncertainty_score":0.8773859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03577703353232935,"score_gpt":0.3032258359677902,"score_spread":0.2674488024354608,"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."}}