{"id":"W2463811999","doi":"10.1111/ele.12647","title":"Convergence and divergence in a long‐term old‐field succession: the importance of spatial scale and species abundance","year":2016,"lang":"en","type":"article","venue":"Ecology Letters","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":154,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Division of Environmental Biology; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Ecological succession; Ecology; Abundance (ecology); Divergence (linguistics); Term (time); Scale (ratio); Convergence (economics); Field (mathematics); Relative abundance distribution; Spatial ecology; Geography; Relative species abundance; Environmental science; Biology; Mathematics; Physics; Cartography","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":[],"consensus_categories":[],"category_scores_codex":[0.0001618876,0.00007847422,0.0001238,0.00001833637,0.0001211201,0.000003320328,0.0001318899,0.00004873657,0.0007369451],"category_scores_gemma":[0.00005190231,0.00004814221,0.00001446869,0.0000596956,0.001011619,0.0001092369,0.0002111919,0.00006897517,0.00001556011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003000043,"about_ca_system_score_gemma":0.000003434847,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009519991,"about_ca_topic_score_gemma":0.02429506,"domain_scores_codex":[0.9993371,0.00005203395,0.0001641641,0.0002171635,0.00006280588,0.0001666679],"domain_scores_gemma":[0.9994664,0.0002996301,0.00009370926,0.0001111238,0.000004163879,0.00002498545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001643817,0.0000135305,0.9938254,0.000005276022,0.000006199141,0.000009087747,0.000323482,0.000006555981,0.005099864,0.00007898001,0.0002228039,0.0003923507],"study_design_scores_gemma":[0.0002458511,0.00004624773,0.9987739,0.00001142787,0.00000558727,0.000005963991,0.00002627475,0.00005838182,0.0005324083,0.0002027882,0.00002435347,0.00006682565],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847582,0.00005988682,0.000418717,0.01427162,0.0001875977,0.0001120857,0.000002510029,0.000005444235,0.000183918],"genre_scores_gemma":[0.9972261,0.0003513243,0.00007465672,0.002055646,0.00001295692,0.00001398837,2.822468e-7,0.000002684116,0.0002623833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02419986,"threshold_uncertainty_score":0.993509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005093157138774053,"score_gpt":0.2111898699346695,"score_spread":0.2060967127958954,"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."}}