{"id":"W2115313781","doi":"10.2193/2006-257","title":"Testing Global Positioning System Performance for Wildlife Monitoring Using Mobile Collars and Known Reference Points","year":2007,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Global Positioning System; Dilution of precision; Computer science; Wildlife; Position (finance); GPS signals; Real-time computing; Geodesy; Residual; Trajectory; Remote sensing; Assisted GPS; Simulation; Geography; GNSS applications; Telecommunications; Algorithm; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001505536,0.0001526844,0.0002107023,0.00007317263,0.0004042181,0.00005803421,0.0001949853,0.00007434914,0.000009426377],"category_scores_gemma":[0.00005034821,0.0001484562,0.00004574991,0.0003144582,0.00007575479,0.0004712813,0.0001576123,0.0001241037,0.000008167719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006755167,"about_ca_system_score_gemma":0.00001899205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002223941,"about_ca_topic_score_gemma":0.000004655598,"domain_scores_codex":[0.9985042,0.00003972138,0.0005774118,0.0002069597,0.0003385396,0.0003332312],"domain_scores_gemma":[0.9990543,0.0001164108,0.0005101433,0.0001249273,0.00006628637,0.0001279999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001136975,0.00004548275,0.9819345,0.0001103815,0.00005476031,0.00003267774,0.00006619732,0.006360986,0.0001255715,0.0001569733,0.0007050706,0.01029365],"study_design_scores_gemma":[0.0008253811,0.0003399552,0.9885493,0.0004550802,0.0001245973,0.0002240442,0.0009281205,0.005421603,0.00005709628,0.0000697194,0.002815864,0.0001892563],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891484,0.0000553711,0.007503008,0.0001700513,0.0004467563,0.0003622038,0.000002473202,0.00002319066,0.002288546],"genre_scores_gemma":[0.9484711,0.00002124358,0.05074018,0.0005005407,0.0001988523,0.000009922385,8.597687e-7,0.00001085457,0.00004650153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04323717,"threshold_uncertainty_score":0.6053867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02532151252244514,"score_gpt":0.2642097488698451,"score_spread":0.2388882363473999,"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."}}