{"id":"W3101711152","doi":"10.22541/au.160525624.40303010/v1","title":"Trait Dissimilarity and Hierarchy Predict Spatial Co-occurrence Patterns of Tree Species in a Subtropical Forest","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; China Scholarship Council; National Natural Science Foundation of China; Sun Yat-sen University; Natural Science Foundation of Guangdong Province","keywords":"Trait; Interspecific competition; Pairwise comparison; Spatial ecology; Competition (biology); Ecology; Biology; Hierarchy; Niche; Statistics; Mathematics; 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":[0.0005095791,0.0002924633,0.0003404936,0.001634055,0.0004014334,0.0004712766,0.0002512143,0.0002722221,0.0006643941],"category_scores_gemma":[0.001312329,0.0001633136,0.000328284,0.001371896,0.0004654451,0.0004042405,0.0005876953,0.0001920364,0.0001018799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00022481,"about_ca_system_score_gemma":0.0001703985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007278141,"about_ca_topic_score_gemma":0.01249951,"domain_scores_codex":[0.9997157,0.00005299938,0.00003047733,0.0001115062,0.00005352235,0.00003570883],"domain_scores_gemma":[0.9987836,0.000361599,0.0004705524,0.00009791365,0.0001215072,0.0001646819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006060312,0.00001533174,0.9896685,0.00001751646,0.00008937362,0.00003044887,0.0001615254,0.0003608055,0.007313995,0.0000435588,0.00002427156,0.002214001],"study_design_scores_gemma":[0.000001704532,0.00001394621,0.9979581,0.0000010584,0.00001192879,0.00003306223,0.00007267419,0.001715279,0.000117347,0.00004409657,0.00002767265,0.000003069488],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995901,0.000032439,0.0002551296,0.000003140336,6.018088e-7,0.000001494994,0.00004456974,0.000004294968,0.00006823771],"genre_scores_gemma":[0.9995977,0.00001111027,0.0002626188,0.000001903272,0.000001525308,0.00000223914,0.00009811371,0.000001215469,0.00002361971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007278141,"threshold_uncertainty_score":0.01447159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02422480636232296,"score_gpt":0.2575099323147451,"score_spread":0.2332851259524221,"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."}}