{"id":"W1983444316","doi":"10.1071/an14428","title":"Temporal dynamics in the foraging decisions of large herbivores","year":2015,"lang":"en","type":"article","venue":"Animal Production Science","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Parks Canada; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Parks Canada","keywords":"Foraging; Optimal foraging theory; Ecology; Herbivore; Forage; Resource distribution; Selection (genetic algorithm); Temporal scales; Biology; Resource allocation; Computer science; Artificial intelligence","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":[0.002713762,0.00004445703,0.00005195378,0.00005051008,0.0001653893,0.00001635432,0.0003314289,0.00001997594,0.00003351867],"category_scores_gemma":[0.0008001648,0.000031727,0.00001243242,0.001024451,0.0004864233,0.0006155978,0.0001049078,0.00007860349,0.00005055571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001307771,"about_ca_system_score_gemma":0.00005156792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002440814,"about_ca_topic_score_gemma":0.00165359,"domain_scores_codex":[0.9990721,0.00004819037,0.0001446217,0.0002263136,0.0003370609,0.0001716762],"domain_scores_gemma":[0.9996481,0.00003452263,0.00006515272,0.0001923078,0.00002795664,0.00003197302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001556144,0.00005810493,0.9941921,5.416551e-7,3.225203e-7,8.869848e-7,0.0007730572,0.0002568876,0.0006158566,0.002057014,0.001430934,0.0005987647],"study_design_scores_gemma":[0.00009645471,0.00007714328,0.9862621,0.00000503189,0.000001836923,0.00001361132,0.002823383,0.006580623,0.0003624435,0.002962759,0.0007613357,0.00005320999],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925305,0.000009234908,0.0003035591,0.004351611,0.0001670948,0.0001164225,6.035386e-7,0.000009196578,0.002511739],"genre_scores_gemma":[0.9988778,0.000001406418,0.000717715,0.0002762026,0.0000236095,0.000006628427,0.000001006725,0.000001743253,0.00009389145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007929901,"threshold_uncertainty_score":0.1792247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03823277075734532,"score_gpt":0.2940949470306339,"score_spread":0.2558621762732886,"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."}}