{"id":"W4213453615","doi":"10.1111/eth.13273","title":"Canada jays (<i>Perisoreus canadensis</i>) identify and exploit coniferous cache locations using visual cues","year":2022,"lang":"en","type":"article","venue":"Ethology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cache; Foraging; Exploit; Sensory cue; Identification (biology); Biology; Selection (genetic algorithm); Computer science; Ecology; Neuroscience; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001802632,0.0003499617,0.0001347243,0.000313228,0.0005996827,0.0003796101,0.0002535218,0.0001856085,0.001817866],"category_scores_gemma":[0.0003009189,0.000164054,0.00007378733,0.0001174907,0.0004931213,0.0002024475,0.000313337,0.0002826788,0.0002647927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003839462,"about_ca_system_score_gemma":0.0003918611,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07292174,"about_ca_topic_score_gemma":0.2664425,"domain_scores_codex":[0.9999167,0.000009705928,0.000003418907,0.00003140957,0.00001419479,0.00002456646],"domain_scores_gemma":[0.9995741,0.00005694499,0.0001191691,0.00003319792,0.00008937575,0.0001271978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006917073,0.0006288954,0.5577651,0.0002291316,0.00009147741,0.00109319,0.004304016,0.0003514633,0.4054973,0.0003760222,0.0009050861,0.02806666],"study_design_scores_gemma":[0.00001170603,0.0005623632,0.9846351,0.00003420451,0.00003072321,0.0009335894,0.003356019,0.0005084503,0.008346178,0.00005597913,0.001504848,0.00002085067],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988613,0.00005038392,0.000090216,0.00001025151,0.000002235305,0.0000107028,0.00004662347,0.000005502005,0.0009227834],"genre_scores_gemma":[0.9977664,0.00004827275,0.0009875422,0.00004010609,0.000001425845,0.0000115685,0.00009370554,0.000003371619,0.001047621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9270782,"threshold_uncertainty_score":0.1449946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01959098054414601,"score_gpt":0.2625630129520152,"score_spread":0.2429720324078692,"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."}}