{"id":"W2010059622","doi":"10.4043/24789-ms","title":"Spatial AHP Enables Highly Effective Pipeline Routing Evaluations","year":2014,"lang":"en","type":"article","venue":"Offshore Technology Conference-Asia","topic":"Offshore Engineering and Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Intecsea (Canada)","funders":"","keywords":"Computer science; Analytic hierarchy process; Geomatics; Pipeline (software); Robustness (evolution); Operations research; Routing (electronic design automation); Geographic information system; Data mining; Engineering; Geography; Remote sensing","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":[0.007936195,0.0009987801,0.001058144,0.002073997,0.001284731,0.002434536,0.001039242,0.0007689793,0.005077252],"category_scores_gemma":[0.01550221,0.0005558999,0.0008068905,0.002224679,0.0006759739,0.001423541,0.002689715,0.001296623,0.0004205999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001215442,"about_ca_system_score_gemma":0.003568508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007281422,"about_ca_topic_score_gemma":0.008820601,"domain_scores_codex":[0.9943221,0.003574819,0.0002559321,0.0002626544,0.001365149,0.0002193848],"domain_scores_gemma":[0.9908291,0.006751732,0.0004222051,0.0005300271,0.001342061,0.0001248949],"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":[0.000118632,0.00009816206,0.001470761,0.0003641522,0.00006553855,0.0001468028,0.0004760001,0.8388852,0.002417297,0.02616942,0.00163354,0.1281545],"study_design_scores_gemma":[0.00002087946,0.00007122777,0.0003046435,0.0000517322,0.00001452929,0.00001930795,0.000368071,0.9754894,0.00119428,0.02046188,0.00198702,0.00001698872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01893585,0.00006510232,0.9736531,0.000188211,0.0000263006,0.0003921147,0.000229064,0.0003777647,0.006132498],"genre_scores_gemma":[0.3081743,0.0001177143,0.6895884,0.00004408569,0.0000150769,0.000574341,0.0002551155,0.00007960932,0.001151489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007936195,"threshold_uncertainty_score":0.04197109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009086249520877656,"score_gpt":0.2267482809685205,"score_spread":0.2176620314476428,"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."}}