{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004010226,0.0002759063,0.0002757007,0.0002529219,0.000217803,0.000628653,0.00027603,0.000488561,0.001889283],"category_scores_gemma":[0.002309573,0.0002150236,0.0003110339,0.0001357092,0.0003882365,0.0005581428,0.0004912616,0.0004906211,0.0001842353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004378824,"about_ca_system_score_gemma":0.0001462378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002557538,"about_ca_topic_score_gemma":0.004104314,"domain_scores_codex":[0.9998678,0.00004194178,0.000006484059,0.00003126625,0.00001588489,0.0000367112],"domain_scores_gemma":[0.9988286,0.0004853315,0.0004397245,0.0000922802,0.00004706371,0.0001069904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009190104,0.0006975638,0.4835722,0.0003201245,0.0004216212,0.001284232,0.0007546946,0.2646618,0.1898877,0.01199991,0.002116918,0.04336419],"study_design_scores_gemma":[0.00007361439,0.0006135833,0.6004258,0.00002625791,0.0001360897,0.0008560179,0.0006973497,0.3805606,0.006121644,0.0088624,0.00155592,0.00007071505],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949109,0.00006596246,0.003307391,0.00008282546,0.000004933126,0.000009099542,0.00003544751,0.00004848775,0.001534915],"genre_scores_gemma":[0.9990164,0.00002820882,0.0006655387,0.00002828098,0.00000237045,0.00000546389,0.00001937276,0.000005641258,0.0002286612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002557538,"threshold_uncertainty_score":0.006320298,"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."}}