{"id":"W2738841895","doi":"10.1002/ecs2.1842","title":"Can subsets of species indicate overall patterns in biodiversity?","year":2017,"lang":"en","type":"article","venue":"Ecosphere","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Marine Fisheries Service; National Research Council Canada; National Oceanic and Atmospheric Administration","keywords":"Species richness; Biodiversity; Ecology; Global biodiversity; Diversity index; Statistics; Sampling (signal processing); Biology; Selection (genetic algorithm); Species diversity; Geography; Mathematics; 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00005688196,0.0000667174,0.00009554538,0.000006527623,0.0001121432,0.000031246,0.0003269867,0.00003962914,0.2210497],"category_scores_gemma":[0.00001609805,0.00006516323,0.00003512919,0.00002900866,0.0001288602,0.000118762,0.0002338017,0.00005521666,0.002458686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001939588,"about_ca_system_score_gemma":0.000003428343,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004604783,"about_ca_topic_score_gemma":0.03210035,"domain_scores_codex":[0.9994664,0.000009686488,0.00009343409,0.0001375545,0.0001326205,0.0001603349],"domain_scores_gemma":[0.9995662,0.000005601097,0.0001082139,0.0002677134,0.000003025017,0.00004922352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005674589,0.00004558682,0.9224209,0.0000054047,0.000002896432,0.00001147956,0.0002063419,0.000002655303,0.000448175,0.0001408904,0.07637197,0.0003380286],"study_design_scores_gemma":[0.0002823067,0.00001300934,0.9819224,0.000006324434,0.000002331705,6.037258e-7,0.0004676,0.000004068921,0.001629236,0.00005907688,0.01553773,0.00007532486],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8320822,0.000007918095,8.533552e-7,0.0009927973,0.00006781038,0.00004947131,0.000230577,0.000007382773,0.1665611],"genre_scores_gemma":[0.9984151,0.00007546788,0.000006764127,0.000123477,0.000006914192,0.000001635163,0.00002827825,0.000002308082,0.001340086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.218591,"threshold_uncertainty_score":0.998318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02776296843232585,"score_gpt":0.2272382488390201,"score_spread":0.1994752804066943,"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."}}