{"id":"W3185249967","doi":"10.23919/acc50511.2021.9482769","title":"Pipeline crawler development for mapping gas pipeline topology","year":2021,"lang":"en","type":"article","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Pipeline (software); Odometer; Inertial measurement unit; Web crawler; Global Positioning System; Computer science; Real-time computing; Geographic information system; Pipeline transport; Topology (electrical circuits); Engineering; Remote sensing; Geography; Artificial intelligence; Mechanical engineering; Electrical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008155127,0.00008643254,0.0001250756,0.00004054575,0.00003887716,0.00002086524,0.00004256239,0.00005781161,0.0002493827],"category_scores_gemma":[0.00001566223,0.00007979495,0.00002763576,0.00008016532,0.000004462754,0.00004484842,0.00001932458,0.00003403336,0.00004038675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003368959,"about_ca_system_score_gemma":0.00002215863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000495912,"about_ca_topic_score_gemma":0.0002032589,"domain_scores_codex":[0.999413,0.000006839128,0.0002402003,0.0001190797,0.00004827439,0.0001726226],"domain_scores_gemma":[0.999742,0.00001727273,0.00001234531,0.000103112,0.00008877515,0.00003646064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001189531,0.00008269437,0.0008480926,0.0006946207,0.0001269912,0.00003219763,0.002529009,0.2605733,0.006863481,0.003523491,0.6879651,0.03674914],"study_design_scores_gemma":[0.0004394501,0.000004898735,0.0001198135,0.00002600824,0.000005378638,0.0000163504,0.0001703265,0.282866,0.07012746,0.00009188266,0.6459397,0.0001927016],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003903236,0.0001971667,0.9749792,0.0003325004,0.0006812797,0.0001181841,0.000002053333,0.0001961231,0.01959024],"genre_scores_gemma":[0.615369,0.0000430655,0.2579337,0.00038381,0.0006456471,0.00009614003,0.000231649,0.00006423701,0.1252328],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7170455,"threshold_uncertainty_score":0.3253944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01893568091686841,"score_gpt":0.2138506519352894,"score_spread":0.194914971018421,"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."}}