{"id":"W2891546128","doi":"10.1101/410126","title":"A framework for disentangling ecological mechanisms underlying the island species-area relationship","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft; McGill University; Alexander von Humboldt-Stiftung; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; National Science Foundation","keywords":"Rarefaction (ecology); Species richness; Habitat; Sampling (signal processing); Ecology; Insular biogeography; Spatial heterogeneity; Biodiversity; Fragmentation (computing); Habitat fragmentation; Physical geography; Geography; Biology; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008809746,0.0003905028,0.0003629375,0.00006070909,0.00115126,0.0001483675,0.0006011056,0.0006150622,0.0004594763],"category_scores_gemma":[0.001108287,0.0003104269,0.0001775322,0.0002841727,0.0004860763,0.000105899,0.0008159644,0.0007651122,0.0001725984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004201448,"about_ca_system_score_gemma":0.00006926579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000667958,"about_ca_topic_score_gemma":0.00008288744,"domain_scores_codex":[0.9978183,0.000155998,0.0004196694,0.0008119312,0.0002729663,0.0005211645],"domain_scores_gemma":[0.99764,0.001072283,0.000379424,0.0007162647,0.0000774817,0.0001145115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001192855,0.0004809308,0.7611933,0.0002580577,0.0005185355,0.00003631207,0.0002802561,0.005305574,0.01623312,0.2118745,0.003698405,0.000001667957],"study_design_scores_gemma":[0.0002701762,0.00009140406,0.9797418,0.0001092479,0.0001629726,1.916188e-8,0.00002790632,0.008391771,0.000551587,0.0093033,0.0007958194,0.0005540125],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6268046,0.00017397,0.3679601,0.001174471,0.001857163,0.001488684,0.0001620411,0.0002432382,0.0001357251],"genre_scores_gemma":[0.9661354,0.00004962597,0.03264045,0.0004460417,0.0001438545,0.0004953139,6.070266e-7,0.00005190998,0.00003677398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3393308,"threshold_uncertainty_score":0.9999348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04691893916091346,"score_gpt":0.262089593333115,"score_spread":0.2151706541722015,"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."}}