{"id":"W2483079406","doi":"","title":"How Often? Towards an Optimum Survey Interval for Mobile Seabeds","year":2015,"lang":"en","type":"article","venue":"The International Hydrographic Review","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Hydrographic Service","funders":"","keywords":"Hydrography; Submarine pipeline; Hydrographic survey; Oceanography; Operations research; Computer science; Geology; Geography; Marine engineering; Remote sensing; 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":[0.00371941,0.0001653365,0.0002549152,0.0001157444,0.00008858614,0.0003084376,0.001379671,0.00004545747,0.0003569612],"category_scores_gemma":[0.0003214449,0.00009995414,0.0001602483,0.0003471497,0.0001661963,0.0003627024,0.00005752972,0.0001844831,0.00007051155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001387904,"about_ca_system_score_gemma":0.0001185773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001451816,"about_ca_topic_score_gemma":0.001788269,"domain_scores_codex":[0.9979562,0.0003125423,0.0002768388,0.0002980059,0.0008399443,0.0003164485],"domain_scores_gemma":[0.9985602,0.0002470767,0.0001012255,0.0003762219,0.0004778969,0.0002373089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004794091,0.0003556423,0.1039245,0.001103813,0.0008578988,0.00003634414,0.0005191704,0.01134541,0.00004758018,0.0002071555,0.06598397,0.8151391],"study_design_scores_gemma":[0.00108582,0.001715477,0.01754189,0.0007686319,0.0001417188,0.0001317428,0.0001801198,0.4012482,0.0000951109,0.007378046,0.569043,0.0006701914],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4260857,0.3261848,0.09393901,0.08813245,0.01076276,0.01771175,0.006117182,0.0008492055,0.03021713],"genre_scores_gemma":[0.9817707,0.01204681,0.001752254,0.001753902,0.0002947017,0.00007276992,0.001229051,0.00001245737,0.001067382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8144689,"threshold_uncertainty_score":0.4076012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1181738618411347,"score_gpt":0.345455627095389,"score_spread":0.2272817652542543,"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."}}