{"id":"W1997285633","doi":"10.1111/j.1365-2699.2008.02017.x","title":"The spatial distribution of vegetation types in the Serengeti ecosystem: the influence of rainfall and topographic relief on vegetation patch characteristics","year":2008,"lang":"en","type":"article","venue":"Journal of Biogeography","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Tanzania Commission for Science and Technology; Smithsonian Institution; National Science Foundation","keywords":"Woodland; Vegetation (pathology); Grassland; Physical geography; Shrubland; Enhanced vegetation index; Environmental science; Spatial heterogeneity; Ecosystem; Geography; Vegetation type; Common spatial pattern; Spatial distribution; Normalized Difference Vegetation Index; Ecology; Leaf area index; Remote sensing; Vegetation Index","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087209,0.00007582155,0.0001389699,0.00006495418,0.0002293246,0.00000890367,0.0001755451,0.00004863573,0.000001305136],"category_scores_gemma":[0.000127986,0.00003820874,0.00007924423,0.0003538597,0.0003984193,0.00009364224,0.00002422141,0.0001596588,0.000001064174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001298898,"about_ca_system_score_gemma":0.000009627009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009420337,"about_ca_topic_score_gemma":0.001255586,"domain_scores_codex":[0.9989451,0.0002011723,0.0004370722,0.00007213399,0.0002504746,0.00009404137],"domain_scores_gemma":[0.9987675,0.0004286789,0.0006075223,0.0001079781,0.00007254695,0.0000157542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007046716,0.00007719386,0.9942129,0.00002307131,0.00003872433,0.000002487702,0.001969666,0.00179018,0.0003710598,0.000429054,0.00001428124,0.00100092],"study_design_scores_gemma":[0.0002116914,0.0003216624,0.997566,0.00004840477,0.00002919774,0.00001597668,0.0001387661,0.0004100481,0.0000665753,0.001038731,0.0001135428,0.00003944562],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987292,0.0003307594,0.0000667147,0.0005711267,0.00008471467,0.0001345972,0.000007828954,0.000001571043,0.00007351596],"genre_scores_gemma":[0.9979753,0.001913136,0.00002112008,0.0000601106,0.0000183889,0.00000482519,0.000003655912,0.000002425223,0.000001062122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003353063,"threshold_uncertainty_score":0.1763803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005415056082327215,"score_gpt":0.2021689928495024,"score_spread":0.1967539367671752,"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."}}