{"id":"W3210550986","doi":"10.1111/2041-210x.13743","title":"Detecting seasonal episodic‐like spatio‐temporal memory patterns using animal movement modelling","year":2021,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Alberta Museum; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Alberta","keywords":"Foraging; Computer science; Set (abstract data type); Resource (disambiguation); Variable (mathematics); Range (aeronautics); Selection (genetic algorithm); Ephemeral key; Home range; Movement (music); Artificial intelligence; Ecology; Biology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001347523,0.0001082286,0.0001581164,0.00004578791,0.0002638222,0.00001005656,0.00005719262,0.0001868499,0.0004620651],"category_scores_gemma":[0.0001096419,0.0001246689,0.00003123528,0.0001712452,0.0001032822,0.000249188,0.000145062,0.0002204032,0.00001042609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003370836,"about_ca_system_score_gemma":0.00003939784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005280083,"about_ca_topic_score_gemma":0.003352247,"domain_scores_codex":[0.9983405,0.0007047689,0.0002519249,0.0003457032,0.00008272069,0.0002743663],"domain_scores_gemma":[0.9995309,0.0002050846,0.00009815425,0.0001062321,0.00001313963,0.0000464559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003478885,0.00004755045,0.9536089,0.000007655346,0.000007888961,0.00001398041,0.0001629605,0.04117215,0.001778966,0.00009190372,0.00001039476,0.003062831],"study_design_scores_gemma":[0.0002067073,0.00003309245,0.6257186,0.000006368145,0.00001143596,0.00001516707,0.0001712909,0.3706602,0.0004137245,0.002653568,0.00002519575,0.00008470882],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7252265,0.0000567804,0.2740262,0.0001851578,0.0002516613,0.00008019413,0.000001316506,0.00001441837,0.0001577477],"genre_scores_gemma":[0.8163866,0.00001270677,0.1829897,0.0004805724,0.00003770213,0.00001555225,0.000007222761,0.000006265946,0.00006374141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.329488,"threshold_uncertainty_score":0.5083849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03749799761204531,"score_gpt":0.3061895254544008,"score_spread":0.2686915278423555,"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."}}