{"id":"W2071682990","doi":"10.1111/1365-2656.12187","title":"Towards an energetic landscape: broad‐scale accelerometry in woodland caribou","year":2013,"lang":"en","type":"article","venue":"Journal of Animal Ecology","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Forest Research Institute; Ministry of Natural Resources and Forestry; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Natural Resources","keywords":"Woodland caribou; Foraging; Ecology; Snow; Woodland; Abundance (ecology); Energetics; Vegetation (pathology); Habitat; Physical geography; Forage; Population; Environmental science; Geography; Biology; Demography","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":[0.0002819929,0.0001857782,0.0005376899,0.0003143496,0.00008028604,0.00004192079,0.0002949039,0.0001889009,0.002284456],"category_scores_gemma":[0.00006187549,0.0001488604,0.0001369964,0.0002096814,0.00008217866,0.0004127747,0.0001241732,0.0004111045,0.00007129483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008336634,"about_ca_system_score_gemma":0.00007395998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004033402,"about_ca_topic_score_gemma":0.0004151358,"domain_scores_codex":[0.9984867,0.000136998,0.000594978,0.0001931724,0.0001883518,0.0003997772],"domain_scores_gemma":[0.9991869,0.00006139166,0.000252527,0.0001280181,0.0002095861,0.0001615992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007898994,0.0004176407,0.9588864,0.00001782042,0.00005471538,0.0005382503,0.0007219664,0.000003126782,0.03396572,0.00007552509,0.002353645,0.002175254],"study_design_scores_gemma":[0.0008988364,0.008822454,0.9871801,0.00001156611,0.00004151569,0.0008455695,0.001211984,0.00002845005,0.0001244844,0.0001631775,0.0005066411,0.000165225],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957017,0.0003227481,0.00000478924,0.000674244,0.0003135223,0.0001036645,0.000008112786,0.00001594661,0.002855239],"genre_scores_gemma":[0.9989597,0.00009318081,0.0004002216,0.0001797249,0.0002883839,0.00001067789,0.000003168457,0.00002216691,0.00004276485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03384123,"threshold_uncertainty_score":0.9986276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.035586345671555,"score_gpt":0.3203129173622371,"score_spread":0.2847265716906821,"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."}}