{"id":"W2587818692","doi":"10.1002/ecy.1773","title":"Behavioral responses to resource heterogeneity can accelerate biological invasions","year":2017,"lang":"en","type":"article","venue":"Ecology","topic":"Forest Insect Ecology and Management","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ecology; Foraging; Resource (disambiguation); Resource distribution; Spatial heterogeneity; Landscape connectivity; Abundance (ecology); Ecosystem; Biological dispersal; Environmental resource management; Geography; Biology; Environmental science; Population; Resource allocation; 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003044467,0.0001129741,0.0001479812,0.00003475448,0.0008653763,0.00003746517,0.0006377344,0.0001393087,0.003511242],"category_scores_gemma":[0.0001903889,0.00009740372,0.00004360273,0.00004462908,0.0004088371,0.00006646791,0.001218969,0.0001246656,0.001784516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001167632,"about_ca_system_score_gemma":0.00001032022,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004528321,"about_ca_topic_score_gemma":0.03300453,"domain_scores_codex":[0.9989105,0.0001439119,0.0001402346,0.0003589909,0.00006582286,0.0003804874],"domain_scores_gemma":[0.999153,0.00006134881,0.00007653963,0.0005591028,0.000003938264,0.0001460426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001028378,0.0001552064,0.9778569,6.612975e-7,0.000007907133,0.00007788322,0.0001206766,0.00007925616,0.003888047,0.0002598799,0.01620272,0.001247979],"study_design_scores_gemma":[0.000181762,0.0004619595,0.9430612,8.565748e-7,0.000006685303,0.00001195773,0.00002174354,0.000006394661,0.0008249241,0.0002980653,0.05500982,0.000114607],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987721,0.000001634654,0.000008324268,0.002730381,0.0002716401,0.0002711721,0.00001054153,0.00004253493,0.008942794],"genre_scores_gemma":[0.9948193,0.000004473627,0.0003786757,0.00247781,0.00003080059,0.0000917229,0.000005780064,0.00000712077,0.002184324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0388071,"threshold_uncertainty_score":0.9989927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1079396010051469,"score_gpt":0.3132131580268721,"score_spread":0.2052735570217251,"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."}}