{"id":"W2518776480","doi":"10.1111/ele.12660","title":"Foraging success under uncertainty: search tradeoffs and optimal space use","year":2016,"lang":"en","type":"article","venue":"Ecology Letters","topic":"Diffusion and Search Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ministerio de Ciencia e Innovación","keywords":"Foraging; Heuristics; Computer science; Optimal foraging theory; Ecology; Exploratory search; Spurious relationship; Process (computing); Theoretical ecology; Machine learning; Biology; Sociology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001617878,0.0001162136,0.0001097039,0.00005695446,0.00009164356,0.00003702337,0.0001402303,0.0001134371,0.00004788929],"category_scores_gemma":[0.00005069267,0.00008675618,0.00004417793,0.00004296971,0.0002594199,0.000009643045,0.0001563011,0.00008732954,0.00001188413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002931752,"about_ca_system_score_gemma":0.00004091751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002854545,"about_ca_topic_score_gemma":0.0001771328,"domain_scores_codex":[0.9990288,0.00009055502,0.0001011899,0.0003231739,0.0000857723,0.0003705298],"domain_scores_gemma":[0.9995655,0.00007119126,0.00002463024,0.0001996225,0.00002657121,0.000112445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001277193,0.00005363965,0.08113758,0.000009356267,0.00008991724,0.00003667172,0.0000621529,0.0006437767,0.9093961,0.0007295746,0.004987158,0.002726407],"study_design_scores_gemma":[0.01052777,0.001510973,0.8058699,0.00007815087,0.0001073659,0.0003486958,0.0006855231,0.006696429,0.05631874,0.000183075,0.11575,0.001923383],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9687129,0.00001920861,0.00617996,0.02477416,0.0001166263,0.0001122823,0.00001173581,0.00001383105,0.00005933912],"genre_scores_gemma":[0.9930543,0.000104716,0.0007143663,0.004730933,0.00007644475,0.000007656789,0.00002190544,0.00001810764,0.001271539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8530773,"threshold_uncertainty_score":0.3537814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0133507994083191,"score_gpt":0.2559210261227762,"score_spread":0.2425702267144571,"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."}}