{"id":"W2114404499","doi":"10.1890/12-1795.1","title":"Estimating extinction from species–area relationships: why the numbers do not add up","year":2013,"lang":"en","type":"article","venue":"Ecology","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Extinction (optical mineralogy); Sampling (signal processing); Habitat; Artifact (error); Ecology; Rare species; Mathematics; Statistics; Remote sensing; Computer science; Paleontology; Geology; Biology; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002375228,0.00009165876,0.0001054549,0.0000167616,0.000673344,0.00002421784,0.0001522157,0.0001067947,0.02595122],"category_scores_gemma":[0.0003553409,0.00007021282,0.00003363015,0.0001026345,0.0003024925,0.0001876803,0.0001408971,0.0002337746,0.005139655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001203121,"about_ca_system_score_gemma":0.000006978301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003127545,"about_ca_topic_score_gemma":0.006796291,"domain_scores_codex":[0.999148,0.0001581943,0.0001893754,0.0002222073,0.000080466,0.0002017455],"domain_scores_gemma":[0.9988483,0.0008227974,0.0001114785,0.0001711911,0.0000126683,0.00003354648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005205351,0.00002707341,0.9336886,0.000001067115,0.00002737516,0.000001183467,0.001679892,0.007441345,0.0002017411,0.000755371,0.05534247,0.0008286439],"study_design_scores_gemma":[0.0001554822,0.0000241317,0.9566455,0.00000153503,0.00001411072,0.000003079467,0.0006225579,0.02745291,0.00001138088,0.01145984,0.003527376,0.00008205508],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9462942,0.00001220023,0.002137912,0.004343917,0.001141049,0.000237477,0.000003979061,0.00004708837,0.04578217],"genre_scores_gemma":[0.9911436,0.000005009237,0.003282165,0.001302372,0.00007336913,0.0001090641,0.00001392062,0.000007072905,0.004063447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0518151,"threshold_uncertainty_score":0.995635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02367026425512314,"score_gpt":0.2314186216231271,"score_spread":0.207748357368004,"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."}}