{"id":"W2101372783","doi":"10.1146/annurev-ecolsys-102710-145006","title":"The Behavioral Ecology of Nutrient Foraging by Plants","year":2011,"lang":"en","type":"article","venue":"Annual Review of Ecology Evolution and Systematics","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":246,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Foraging; Ecology; Biology; Behavioral ecology; Population; Nutrient; Resource (disambiguation); Optimal foraging theory; Selection (genetic algorithm); Computer science","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":[0.0005296,0.00007743888,0.0003656299,0.000005440512,0.0001490136,0.000002258471,0.0001222957,0.0000545184,0.00002615897],"category_scores_gemma":[0.00008942438,0.00002495434,0.0000574624,0.00006551216,0.0001283513,0.00003354658,0.00005748389,0.00004337094,0.000004510305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001152583,"about_ca_system_score_gemma":0.000005565912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004340284,"about_ca_topic_score_gemma":0.0005617138,"domain_scores_codex":[0.9990916,0.0001341005,0.0004561444,0.00009153468,0.00008196163,0.000144647],"domain_scores_gemma":[0.9992549,0.00025865,0.0003296859,0.00002411447,0.0001020922,0.00003056872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002005468,0.00169123,0.7517546,0.02506357,0.0004231138,0.00001601313,0.00338903,9.594845e-8,0.01023159,0.06927206,0.1185519,0.0194063],"study_design_scores_gemma":[0.0003964676,0.003073276,0.9518234,0.00667298,0.0002873469,0.0001144769,0.01068579,0.00005906517,0.0004730828,0.002804485,0.02321049,0.0003991141],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9527947,0.04539009,0.000003314606,0.0002358147,0.0001029439,0.0004253585,0.0001721786,0.000009281538,0.0008662804],"genre_scores_gemma":[0.9778817,0.02189149,0.00001986619,0.00006798167,0.00001476947,0.00001939919,0.000008252399,3.768374e-7,0.00009615356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2000689,"threshold_uncertainty_score":0.1146107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05344780018615714,"score_gpt":0.2573346666797806,"score_spread":0.2038868664936234,"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."}}