{"id":"W2903884965","doi":"10.3390/drones2040044","title":"Estimating Wildlife Tag Location Errors from a VHF Receiver Mounted on a Drone","year":2018,"lang":"en","type":"article","venue":"Drones","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Defence Research and Development Canada; Environment and Climate Change Canada; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Drone; Transmitter; Ranging; Range (aeronautics); Global Positioning System; Computer science; Remote sensing; SIGNAL (programming language); Real-time computing; Geography; Telecommunications; Engineering","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000141641,0.0001078684,0.00009793099,0.00002562133,0.0002286791,0.00001558282,0.0001379993,0.00009094836,0.002300086],"category_scores_gemma":[0.0001390199,0.0001047124,0.00001960978,0.0002003701,0.0002271802,0.0002249438,0.00005384582,0.00008788456,0.004449719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001117363,"about_ca_system_score_gemma":0.00001209302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001173955,"about_ca_topic_score_gemma":0.0007374308,"domain_scores_codex":[0.9991579,0.00005638051,0.0001598512,0.0002845951,0.0001637754,0.0001775117],"domain_scores_gemma":[0.9995526,0.00007505013,0.00009511301,0.000213463,0.00001477674,0.00004898656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000151798,0.0002178575,0.9235997,0.000005917267,0.00003476159,0.000005940984,0.002473349,0.004269789,0.002613365,0.0003004994,0.05399334,0.01233373],"study_design_scores_gemma":[0.0003074433,0.0001390694,0.914793,0.0000309355,0.00001526513,0.000001659452,0.00009734797,0.07814103,0.0004429687,0.001860389,0.004007883,0.0001630048],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912678,0.000005150411,0.002552912,0.002599639,0.0003943992,0.0001337531,0.000004084405,0.00007812079,0.00296415],"genre_scores_gemma":[0.9909376,0.000001058631,0.004296347,0.003677668,0.0002144814,0.00002443098,0.00003329225,0.0000111836,0.0008039365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07387125,"threshold_uncertainty_score":0.9986119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01230332599552091,"score_gpt":0.2438417461555907,"score_spread":0.2315384201600698,"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."}}