{"id":"W4405733362","doi":"10.1111/geb.13952","title":"Sampling Simulation in a Virtual Ocean Reveals Strong Sampling Effect in Marine Diversity Patterns","year":2024,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Marine Biology and Ecology Research","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Ministério da Ciência, Tecnologia, Inovações e Comunicações; Natural Sciences and Engineering Research Council of Canada; European Commission; Fundação de Amparo à Pesquisa do Estado de Goiás; Jarislowsky Foundation; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Agencia Estatal de Investigación; Ministerio de Ciencia, Innovación y Universidades","keywords":"Species richness; Undersampling; Sampling (signal processing); Macroecology; Sampling bias; Estimator; Null model; Ecology; Statistics; Species diversity; Marine protected area; Rarefaction (ecology); Biology; Sample size determination; Habitat; Mathematics; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003841115,0.0002200287,0.0003563739,0.0004848611,0.0004294651,0.0008536109,0.0007370622,0.0005886085,0.0009270068],"category_scores_gemma":[0.01603036,0.0002399336,0.0005358423,0.000463122,0.000791283,0.0006805521,0.000669272,0.0005760558,0.00008151964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007517561,"about_ca_system_score_gemma":0.0005746481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01447902,"about_ca_topic_score_gemma":0.00980821,"domain_scores_codex":[0.9988097,0.000745255,0.00006604083,0.0001846205,0.00009206503,0.0001023583],"domain_scores_gemma":[0.9836376,0.01276891,0.001166376,0.001392492,0.0006745731,0.0003600452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002240373,0.00009661129,0.1666365,0.00004788415,0.0001435924,0.0001353876,0.0003496762,0.8205538,0.001253414,0.004358121,0.0003128788,0.005888151],"study_design_scores_gemma":[0.0000218336,0.0000743885,0.01763487,0.00001271181,0.00002523378,0.00002639006,0.0001137231,0.9800853,0.0004936595,0.00132301,0.0001764698,0.00001238567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913133,0.00002993748,0.008023146,0.00006281459,0.000006740221,0.000009117918,0.00005793118,0.00004170144,0.0004553339],"genre_scores_gemma":[0.9983424,0.00001036441,0.001475797,0.00001546061,0.000001385651,0.000009139726,0.00006035942,0.000005600563,0.00007933786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01447902,"threshold_uncertainty_score":0.02878946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01933606997921763,"score_gpt":0.2758236573344552,"score_spread":0.2564875873552376,"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."}}