{"id":"W2598225981","doi":"10.1038/srep45127","title":"Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Turtle Biology and Conservation","field":"Environmental Science","cited_by":220,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada; International Fund for Animal Welfare","keywords":"Wildlife; Aerial survey; Population; Computer science; Aerial imagery; Fur seal; Photogrammetry; Remote sensing; Artificial intelligence; Computer vision; Ecology; Geography; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006765199,0.00006326387,0.00008765995,0.00002937775,0.0005948594,0.0001551888,0.0000454894,0.00005718245,0.00003022513],"category_scores_gemma":[0.00008071349,0.0000552964,0.00001341188,0.00004945182,0.0004587368,0.0003978987,0.0001224117,0.00003547648,0.000002960316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002183737,"about_ca_system_score_gemma":0.00001080551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006762581,"about_ca_topic_score_gemma":0.0001112323,"domain_scores_codex":[0.9992314,0.00003860008,0.0002082954,0.0002960123,0.000127724,0.0000979542],"domain_scores_gemma":[0.999275,0.00001010861,0.0003342771,0.0003274495,0.00001747118,0.00003570886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000009372981,0.0000109573,0.3819487,0.00001131603,0.000005053367,0.00001658277,0.00007589796,0.0002358117,0.6141724,0.000004708952,0.0001260488,0.003383123],"study_design_scores_gemma":[0.00009067211,0.00001841652,0.9219671,0.00001563944,0.00001502282,0.00016061,0.00002428385,0.05757005,0.01908625,0.0002536668,0.0007115383,0.00008674412],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978444,0.00002593992,0.000152107,0.00005375051,0.001359681,0.0001410606,6.216924e-7,0.00003888332,0.0003835818],"genre_scores_gemma":[0.9994437,0.000001665575,0.0001704039,0.00001286796,0.00001779274,0.000003745987,0.000005428711,0.000003586257,0.0003407834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5950862,"threshold_uncertainty_score":0.4575237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01221201314668066,"score_gpt":0.23102266443265,"score_spread":0.2188106512859693,"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."}}