{"id":"W2042334266","doi":"10.1371/journal.pone.0122721","title":"Predicting Plant Diversity Patterns in Madagascar: Understanding the Effects of Climate and Land Cover Change in a Biodiversity Hotspot","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Biodiversity; Climate change; Biodiversity hotspot; Land cover; Geography; Ecology; Land use; Ecosystem; Biological dispersal; Habitat; Tropics; Agroforestry; Environmental science; Biology; Population","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":[0.0002116846,0.00006330648,0.0001140284,0.00002853729,0.00007594901,0.00001029299,0.00009179737,0.0000354363,0.0003785465],"category_scores_gemma":[0.00003328187,0.00005181359,0.00001233603,0.0001194007,0.00007577839,0.000116281,0.0005282435,0.00007459339,0.00002578086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004293661,"about_ca_system_score_gemma":0.000001711493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003393581,"about_ca_topic_score_gemma":0.006834724,"domain_scores_codex":[0.9993458,0.00004345204,0.00007493678,0.0001273524,0.0002361244,0.0001722995],"domain_scores_gemma":[0.9997622,0.00005751092,0.00004616002,0.00007724435,0.000002980926,0.00005394818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003968299,0.0001779118,0.9958721,0.0000637921,0.000007558176,0.00001383399,0.003561002,8.995571e-7,0.0001931391,0.00001240805,0.00004707371,0.000010599],"study_design_scores_gemma":[0.0008232675,0.00006089214,0.9957722,0.00009286927,0.00002040596,8.025586e-7,0.002554753,0.0002445115,0.0003441336,0.00001913533,0.000005671605,0.00006139499],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985377,0.00003873983,0.000002248545,0.0001957302,0.0000276704,0.0002272823,0.0002243407,0.00001019613,0.0007361443],"genre_scores_gemma":[0.9995273,0.0003224578,0.000002395336,0.0001137729,0.000005719633,0.000004156048,0.0000182272,0.000001773378,0.00000416285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003441143,"threshold_uncertainty_score":0.5130101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1129676567890997,"score_gpt":0.2198610872207235,"score_spread":0.1068934304316238,"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."}}