{"id":"W2258320707","doi":"10.1139/juvs-2015-0017","title":"Assessment of known impacts of unmanned aerial systems (UAS) on marine mammals: data gaps and recommendations for researchers in the United States","year":2016,"lang":"en","type":"article","venue":"Journal of Unmanned Vehicle Systems","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Marine Fisheries Service; National Oceanic and Atmospheric Administration","keywords":"Citizen science; Wildlife; Limiting; Resource (disambiguation); Environmental resource management; Dissemination; Environmental planning; Business; Computer science; Ecology; Engineering; Geography; Environmental science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05946713,0.001346383,0.002504746,0.008349977,0.002424046,0.006553707,0.004512112,0.003485058,0.005115831],"category_scores_gemma":[0.1080605,0.0007603626,0.001892864,0.006783269,0.003452895,0.01314583,0.005232747,0.00359852,0.0007198477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005308065,"about_ca_system_score_gemma":0.0281745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04636528,"about_ca_topic_score_gemma":0.08030351,"domain_scores_codex":[0.9777208,0.009961743,0.005077371,0.001849481,0.004605288,0.0007852618],"domain_scores_gemma":[0.8023698,0.1250187,0.01505758,0.005947758,0.04675584,0.004850267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0003230157,0.0003845329,0.07442448,0.01909293,0.0006970332,0.0003224449,0.003875113,0.002853065,0.001407538,0.008048099,0.06532454,0.8232473],"study_design_scores_gemma":[0.0001140604,0.001253139,0.1614364,0.1495652,0.002573255,0.000686651,0.07382578,0.006789041,0.003333336,0.07858323,0.5214287,0.0004112289],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.03733458,0.4902502,0.01962069,0.4092404,0.004242541,0.0009759887,0.008281588,0.0004315825,0.02962246],"genre_scores_gemma":[0.2996674,0.5213184,0.1100082,0.05292356,0.002406021,0.001957278,0.00784132,0.0001164993,0.003761221],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.05946713,"threshold_uncertainty_score":0.314496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.112332072076914,"score_gpt":0.3689205906479599,"score_spread":0.2565885185710459,"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."}}