{"id":"W4385886281","doi":"10.55274/r0011530","title":"PR-271-143716-R02 Bayesian Belief Network (BBN) Decision Support for Pipeline Third Party Interference","year":2018,"lang":"en","type":"report","venue":"","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering","funders":"","keywords":"Pipeline (software); Computer science; Bayesian network; False positive paradox; Data mining; Decision support system; Remote sensing; Computer security; Artificial intelligence; Geography","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":[0.00246761,0.0008161767,0.0006321996,0.001171727,0.0005511278,0.002693111,0.001266025,0.00184373,0.188147],"category_scores_gemma":[0.006523909,0.0003081538,0.0004935977,0.0009471073,0.0004867952,0.001520454,0.001196405,0.001007857,0.07357638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001547691,"about_ca_system_score_gemma":0.001694398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01571554,"about_ca_topic_score_gemma":0.01192051,"domain_scores_codex":[0.9985824,0.0005561569,0.00007677409,0.0002355517,0.0004477371,0.0001014462],"domain_scores_gemma":[0.9973138,0.001338165,0.0001032327,0.0002726177,0.0008417246,0.0001304188],"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":[0.0009811224,0.0004503392,0.001982417,0.0002492166,0.00006637191,0.0003510863,0.00009868885,0.1361747,0.004925834,0.03702946,0.2173346,0.6003562],"study_design_scores_gemma":[0.0002652318,0.0001973725,0.0005654541,0.0001065993,0.00002112076,0.0001095255,0.00006714698,0.8239067,0.003405977,0.01284997,0.1584716,0.00003329366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01114631,0.0002411989,0.6229091,0.002461534,0.0003674662,0.0007849227,0.005221809,0.02246233,0.3344053],"genre_scores_gemma":[0.216944,0.0005080161,0.4232413,0.001016416,0.0002462166,0.0007077256,0.01095197,0.002578037,0.3438064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.188147,"threshold_uncertainty_score":0.6294146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1185945864416552,"score_gpt":0.4108487268622384,"score_spread":0.2922541404205833,"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."}}