{"id":"W2786373385","doi":"","title":"Error Budget Analysis for surface and underwater survey system","year":2016,"lang":"en","type":"article","venue":"The International Hydrographic Review","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Interdisciplinary Centre for the Development of Ocean Mapping","funders":"","keywords":"Subsea; Hydrographic survey; Bathymetry; Hydrography; Depth sounding; Underwater; Marine engineering; Geological survey; Computer science; Environmental science; Engineering; Operations research; Geography; Geology; Oceanography; Cartography","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":[0.002537244,0.0007265218,0.0006542269,0.001569474,0.0004756564,0.0008801813,0.0007435406,0.0006154551,0.002447179],"category_scores_gemma":[0.01249315,0.0003019396,0.0005624879,0.0009874743,0.0003669326,0.001975723,0.001236508,0.0005895945,0.0004246939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001129595,"about_ca_system_score_gemma":0.001153919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0105248,"about_ca_topic_score_gemma":0.003915971,"domain_scores_codex":[0.9976305,0.0005190494,0.0001812504,0.000366163,0.001107427,0.0001956336],"domain_scores_gemma":[0.9922469,0.003802718,0.0006087738,0.0005070146,0.002747437,0.00008711583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004487024,0.00006931734,0.01377872,0.0005590114,0.0001352568,0.0002172088,0.0003660905,0.7658272,0.02174338,0.01858338,0.002643705,0.1756281],"study_design_scores_gemma":[0.000004832936,0.00005121174,0.003172718,0.00003761141,0.00003016089,0.0001076381,0.00005612464,0.9841526,0.009029768,0.001972359,0.001361642,0.00002330773],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0349432,0.0006917032,0.9611407,0.0001562125,0.00003300954,0.00004439759,0.000148308,0.0003007828,0.002541798],"genre_scores_gemma":[0.8517978,0.001096274,0.1398022,0.0001002222,0.00006046616,0.0001750244,0.000931141,0.0003277507,0.005709125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0105248,"threshold_uncertainty_score":0.02092707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04526968152112918,"score_gpt":0.332777021643777,"score_spread":0.2875073401226478,"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."}}