{"id":"W4321095154","doi":"10.1002/wlb3.01042","title":"A meta‐analysis of shrub density as a predictor of animal abundance","year":2023,"lang":"en","type":"article","venue":"Wildlife Biology","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Shrub; Abundance (ecology); Ecology; Habitat; Biology; Relative species abundance; Shrubland","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":[0.01137469,0.00212629,0.00496908,0.003628116,0.0006641535,0.00233425,0.001880191,0.00157316,0.003528228],"category_scores_gemma":[0.0185084,0.000911267,0.02881859,0.004675079,0.0004954681,0.001168921,0.0011536,0.002095919,0.0005018535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008703504,"about_ca_system_score_gemma":0.001383559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006314015,"about_ca_topic_score_gemma":0.01088798,"domain_scores_codex":[0.9934571,0.003754031,0.0008311587,0.001348162,0.0003926509,0.0002170005],"domain_scores_gemma":[0.9852126,0.01147247,0.001020257,0.001455656,0.0005932668,0.0002458541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.00126512,0.00002844379,0.03397844,0.02281583,0.929383,0.0001894058,0.00005828467,0.0009676543,0.0008927271,0.0002858439,0.0009070039,0.00922822],"study_design_scores_gemma":[0.0001855288,0.0002071139,0.02201608,0.002084162,0.9711014,0.0001179976,0.00004714615,0.001111895,0.0004782417,0.0005838288,0.002046454,0.00002014564],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.08031716,0.8956264,0.01467311,0.001619852,0.001083918,0.0001957896,0.004759698,0.0003379586,0.00138614],"genre_scores_gemma":[0.8661118,0.1180971,0.009923686,0.001220256,0.00040586,0.0004015089,0.002736183,0.0001588929,0.0009448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01137469,"threshold_uncertainty_score":0.06015587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03412665474587751,"score_gpt":0.2856716372556778,"score_spread":0.2515449825098003,"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."}}