{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002537409,0.0003267344,0.0006498109,0.0008005019,0.0005029011,0.002846237,0.0005396635,0.00123627,0.001797569],"category_scores_gemma":[0.02381344,0.0002623929,0.000401007,0.000722003,0.001742051,0.003691955,0.0016354,0.0008087349,0.0001614028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001069553,"about_ca_system_score_gemma":0.0005136585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001021392,"about_ca_topic_score_gemma":0.0008596874,"domain_scores_codex":[0.9990741,0.0004456286,0.00004495807,0.0001379523,0.0001571597,0.0001401993],"domain_scores_gemma":[0.9893566,0.008191725,0.001309143,0.0004240918,0.0002650768,0.0004533288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001463299,0.0002521938,0.08792754,0.0005702608,0.0004162362,0.0009724925,0.00289612,0.3641546,0.01854466,0.4063919,0.002313179,0.1140975],"study_design_scores_gemma":[0.0000543162,0.0003142008,0.0488814,0.00009459724,0.00009459291,0.0004804191,0.001348413,0.4332546,0.001715614,0.51219,0.001450453,0.0001212905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9606856,0.001677748,0.02839832,0.001709159,0.00002101397,0.00001604026,0.00006273863,0.00002767856,0.007401662],"genre_scores_gemma":[0.99749,0.0002634889,0.001920551,0.00002442494,0.000009980155,0.000009503462,0.00001442202,0.000007456529,0.0002600231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002846237,"threshold_uncertainty_score":0.01341927,"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."}}