{"id":"W2466668469","doi":"10.1061/9780784479957.045","title":"New Technologies and Applications of a Multi-Sensor Condition Assessment for Large-Diameter Underground Pipe Infrastructure","year":2016,"lang":"en","type":"article","venue":"Pipelines 2016","topic":"Geotechnical Engineering and Underground Structures","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Syndicat Interdépartemental pour l’Assainissement de l’Agglomération Parisienne","keywords":"Lidar; Marine engineering; Environmental science; Sanitary sewer; Software deployment; Computer science; Remote sensing; Engineering; Geology; Environmental engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005565383,0.0006818473,0.0003121649,0.001664556,0.0002423019,0.0008911565,0.0007338477,0.0008641359,0.002084837],"category_scores_gemma":[0.0007154772,0.0003882328,0.0003598649,0.0009396614,0.0004157111,0.001849176,0.0009473076,0.000701183,0.000805756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005279447,"about_ca_system_score_gemma":0.0004109287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007580951,"about_ca_topic_score_gemma":0.001107833,"domain_scores_codex":[0.9991079,0.00008575317,0.00002873802,0.0001904264,0.0005476368,0.00003954467],"domain_scores_gemma":[0.9992478,0.0001188624,0.000105103,0.0001363977,0.0003279874,0.0000638499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001145337,0.0001096566,0.009218375,0.0003910299,0.0000420133,0.0004331738,0.0004098998,0.01551825,0.3750423,0.009542375,0.003617241,0.585561],"study_design_scores_gemma":[0.00003892766,0.001547238,0.04322878,0.0003244605,0.0001410334,0.003393189,0.000662133,0.3090875,0.4172731,0.0155317,0.208422,0.0003498579],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05986689,0.00354414,0.9162763,0.0008498639,0.0003191619,0.0001501567,0.0004242231,0.002685721,0.01588368],"genre_scores_gemma":[0.4905452,0.0029241,0.4955677,0.0003326855,0.0002087676,0.000132367,0.0004258685,0.0001520109,0.009711379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002084837,"threshold_uncertainty_score":0.006974518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008332861601152462,"score_gpt":0.2524689883354609,"score_spread":0.2441361267343084,"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."}}