{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002175593,0.00008382906,0.0001118348,0.000025851,0.0006096558,0.000008991749,0.000121267,0.00005199395,0.001564336],"category_scores_gemma":[0.0000434432,0.00009827792,0.00001254745,0.0001317444,0.0002005744,0.00007792765,0.0002583469,0.0002068318,0.00001313371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003780548,"about_ca_system_score_gemma":0.0002299253,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7677928,"about_ca_topic_score_gemma":0.9134947,"domain_scores_codex":[0.9990147,0.0002303204,0.0001493823,0.0002466292,0.0001350578,0.0002239307],"domain_scores_gemma":[0.9996447,0.00009820163,0.0000622121,0.0001254996,0.000006889428,0.00006247836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001181673,0.00002991802,0.975538,0.000002386186,0.00001418062,0.000101479,0.0006975945,0.002934737,0.002304796,0.0001713462,0.01741006,0.0007836684],"study_design_scores_gemma":[0.0001951253,0.00005716743,0.9782889,8.342807e-7,0.00002362358,0.0002946567,0.001551683,0.001298553,0.000133298,0.0003234879,0.01767798,0.0001546427],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953895,0.00008122432,0.0001813902,0.003356349,0.000324392,0.0001011041,0.00001410666,0.00001710363,0.0005348565],"genre_scores_gemma":[0.9940404,0.00000527791,0.000251171,0.005034721,0.00002109553,0.00003922706,0.00001551606,0.00000870027,0.0005838325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1457019,"threshold_uncertainty_score":0.9993483,"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."}}