{"id":"W2158211309","doi":"10.1890/04-0895","title":"GPS MEASUREMENT ERROR INFLUENCES ON MOVEMENT MODEL PARAMETERIZATION","year":2005,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Directorate for Biological Sciences; National Science Foundation","keywords":"Global Positioning System; Observational error; Monte Carlo method; Computer science; Data quality; Statistics; Accuracy and precision; Length measurement; Simulation; Geodesy; Mathematics; Geography; Engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000149126,0.0001431287,0.0001434472,0.00003538923,0.0003116251,0.00002335595,0.0001697358,0.00007420564,0.0003422969],"category_scores_gemma":[0.00003066111,0.0001108056,0.00006648398,0.0001174968,0.00006995469,0.00006799902,0.00007594332,0.0001059995,0.0006576208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001557666,"about_ca_system_score_gemma":0.00001575473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000046088,"about_ca_topic_score_gemma":0.00001337069,"domain_scores_codex":[0.9989025,0.00002912035,0.000260466,0.0003185621,0.0002777749,0.0002115926],"domain_scores_gemma":[0.9995095,0.00003385324,0.00007343357,0.0002137219,0.0001020236,0.0000675022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0009673963,0.01569196,0.06325849,0.0001023119,0.0003687533,0.00002292863,0.001558067,0.0790874,0.2655613,0.4016244,0.01750094,0.1542561],"study_design_scores_gemma":[0.0004217939,0.001216072,0.9518741,0.00001096659,0.00006615858,0.000002701456,0.0001393694,0.01232536,0.0006187448,0.007429587,0.02545787,0.0004372924],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.958499,0.00007326794,0.01790191,0.004306122,0.00003364605,0.001265523,0.00005029815,0.0003314608,0.0175388],"genre_scores_gemma":[0.9927775,0.00001734335,0.003323559,0.001447664,0.00007751541,0.002204336,0.00001203157,0.000009069498,0.0001309753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8886156,"threshold_uncertainty_score":0.8452605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1679757449292385,"score_gpt":0.3652584380061892,"score_spread":0.1972826930769507,"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."}}