{"id":"W2968443045","doi":"10.1111/geb.12981","title":"Spatial analyses of multi‐trophic terrestrial vertebrate assemblages in Europe","year":2019,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Natural Resources Canada; Canadian Forest Service","funders":"Agence Nationale de la Recherche","keywords":"Food web; Species richness; Trophic level; Ecology; Spatial distribution; Spatial variability; Biodiversity; Spatial analysis; Geography; Biology; Statistics; Remote sensing","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":[0.0007506711,0.0001941843,0.0002684463,0.002974614,0.0002001241,0.0004676386,0.0001847092,0.000177253,0.000623173],"category_scores_gemma":[0.001803133,0.0001477688,0.000415848,0.002389024,0.0002164574,0.0003039322,0.000649445,0.00008968139,0.00008273421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002045484,"about_ca_system_score_gemma":0.0001087253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005307923,"about_ca_topic_score_gemma":0.008036243,"domain_scores_codex":[0.9994831,0.0001309047,0.0000594401,0.0002100172,0.00007258209,0.00004389548],"domain_scores_gemma":[0.998417,0.000547911,0.00056036,0.0001385728,0.0002425876,0.0000935198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000603421,0.000007123588,0.9871227,0.00004162015,0.0002323465,0.00006434677,0.0001285509,0.001394432,0.00292071,0.0001058841,0.00005838309,0.007863608],"study_design_scores_gemma":[0.000001386309,0.000008258306,0.999071,0.000005406156,0.000012477,0.00004828574,0.00006517807,0.0005571099,0.0000919544,0.00003526463,0.0001021165,0.000001708051],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991074,0.0001701165,0.0003452099,0.000004993159,7.069147e-7,0.00000114418,0.0002093179,0.000004305115,0.0001567393],"genre_scores_gemma":[0.9988404,0.00006652028,0.0005460828,0.000003122559,0.00000153235,0.000002805629,0.000488882,0.000002010496,0.00004865702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005307923,"threshold_uncertainty_score":0.01055402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01240197836251568,"score_gpt":0.2633599165481622,"score_spread":0.2509579381856465,"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."}}