{"id":"W2148633364","doi":"10.1002/jwmg.259","title":"Time geography and wildlife home range delineation","year":2011,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Home range; Wildlife; Range (aeronautics); Geography; Context (archaeology); Time geography; Measure (data warehouse); Movement (music); Computer science; Environmental resource management; Data science; Ecology; Environmental science; Human geography; Data mining; Habitat; Engineering; Economic geography; Historical geography; Biology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006409535,0.0001277803,0.0001799793,0.0001455184,0.0001083039,0.0000184671,0.0002038304,0.00006156218,0.001077326],"category_scores_gemma":[0.00001710641,0.0001114513,0.00008215955,0.0001998171,0.0001547766,0.0004339972,0.0001352128,0.0001226343,0.0002416155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005093836,"about_ca_system_score_gemma":0.000004878447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002176947,"about_ca_topic_score_gemma":0.000006054744,"domain_scores_codex":[0.9988679,0.00006982753,0.0004181264,0.0001611005,0.0002959998,0.0001870959],"domain_scores_gemma":[0.9993669,0.00002596301,0.0003222593,0.0001482316,0.00002333692,0.0001132558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001257883,0.0001546663,0.8941565,0.00001287813,0.0001137913,0.00004479772,0.0003392684,0.0001195397,0.00001175895,0.0001446485,0.09426762,0.01050873],"study_design_scores_gemma":[0.0007196407,0.0001899775,0.9573691,0.00002200311,0.0001049995,0.00003538532,0.0001025507,0.0002749135,0.000004478823,0.0008467075,0.04019663,0.0001336139],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878418,0.00005631745,0.001487298,0.00287296,0.0002346929,0.0001770842,0.00000111788,0.00001916881,0.007309581],"genre_scores_gemma":[0.9568573,0.0006272863,0.01480295,0.02531026,0.0002453019,0.00001581338,0.000004390527,0.00002938321,0.00210731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06321259,"threshold_uncertainty_score":0.9998358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151889073780764,"score_gpt":0.1964882996893785,"score_spread":0.1849694089515709,"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."}}