{"id":"W2070292368","doi":"10.4043/25381-ms","title":"AUV-Based 3D Laser Inspection for Structural Integrity Management in Deepwater Fields","year":2014,"lang":"en","type":"article","venue":"Offshore Technology Conference","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Subsea; Lidar; Underwater; Sonar; Computer science; Marine engineering; 3D printing; 3D modeling; Change detection; Systems engineering; Remote sensing; Engineering; Artificial intelligence; Geology; Mechanical 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":[0.0002107882,0.0001276865,0.0001548354,0.0001402045,0.0001063626,0.00003260945,0.0002516637,0.0002168092,0.0003222221],"category_scores_gemma":[0.00004317587,0.00009637542,0.00003036012,0.0002004796,0.0001217977,0.0000799431,0.00001380761,0.0002768096,0.00004258986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007653205,"about_ca_system_score_gemma":0.00001427139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002611291,"about_ca_topic_score_gemma":0.01384907,"domain_scores_codex":[0.9991658,0.0000437449,0.0001593001,0.0002765318,0.00008525045,0.0002694093],"domain_scores_gemma":[0.9996356,0.00004925771,0.00003899025,0.0001959721,0.00004671944,0.00003339462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007237631,0.00001312309,0.702635,0.00004261664,0.00001334192,0.000007378906,0.00007302698,0.0003411802,0.00003363682,0.002684282,0.0003102098,0.2937738],"study_design_scores_gemma":[0.001260379,0.0005016972,0.7909852,0.00007621433,0.00002208412,0.000009673902,0.0004813227,0.1701171,0.002560088,0.02656208,0.006915945,0.0005082579],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889702,0.0000305204,0.004051719,0.001040298,0.0002647927,0.0002405453,0.00001645119,0.0002532123,0.005132225],"genre_scores_gemma":[0.9948794,0.000006423596,0.004657504,0.0001591892,0.0000233168,0.00001148094,0.0000833057,0.00000253086,0.0001768303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2932655,"threshold_uncertainty_score":0.7728108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02163927576817793,"score_gpt":0.2386801768175877,"score_spread":0.2170409010494098,"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."}}