{"id":"W2801921165","doi":"10.1139/juvs-2018-0002","title":"Performance of manned and unmanned aerial surveys to collect visual data and imagery for estimating arctic cetacean density and associated uncertainty","year":2018,"lang":"en","type":"article","venue":"Journal of Unmanned Vehicle Systems","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Marine Fisheries Service; Office of Naval Research; Bureau of Ocean Energy Management; U.S. Fish and Wildlife Service; U.S. Navy; Office of Science; National Oceanic and Atmospheric Administration; Office of Marine and Aviation Operations; U.S. Department of the Interior","keywords":"Aerial survey; Marine mammal; Drone; Transect; Remote sensing; Arctic; Environmental science; Geography; Oceanography; Fishery; Geology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003803973,0.0001935537,0.0006190728,0.00006496655,0.0003233817,0.00007827609,0.0002518436,0.00007047687,0.00001746476],"category_scores_gemma":[0.0008711435,0.0001654869,0.00003793449,0.000220529,0.0002452679,0.000379129,0.0007222501,0.000116779,0.000003039031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001233968,"about_ca_system_score_gemma":0.00002476782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001050703,"about_ca_topic_score_gemma":0.0009844361,"domain_scores_codex":[0.9980541,0.0002724979,0.0006802255,0.0003268054,0.0003535544,0.0003127479],"domain_scores_gemma":[0.9984378,0.0003407054,0.0006594792,0.0002088554,0.0001652244,0.0001879669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001371621,0.0001794922,0.9710348,0.0007826358,0.0003504535,0.00001600639,0.0008724306,0.0005978165,0.01042557,0.00001180313,0.002379104,0.01197824],"study_design_scores_gemma":[0.002267123,0.003452093,0.7039224,0.0004963716,0.0001489899,0.0001100717,0.0003734937,0.2881359,0.0004265264,0.00002604524,0.0003315152,0.0003094868],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985189,0.0001691681,0.0002814591,0.0001498133,0.0002709777,0.0004718186,0.00002800792,0.000009109884,0.0001007751],"genre_scores_gemma":[0.9983324,0.00007282274,0.001263151,0.00004646686,0.0001976807,0.000003525225,0.000004157265,0.00001837628,0.00006137899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2875381,"threshold_uncertainty_score":0.6748362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02827137282026032,"score_gpt":0.2804134073836754,"score_spread":0.2521420345634151,"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."}}