{"id":"W2561009677","doi":"10.1093/biosci/biw150","title":"Combining Biodiversity Resurveys across Regions to Advance Global Change Research","year":2016,"lang":"en","type":"article","venue":"BioScience","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Institute of Botany of the Czech Academy of Sciences; Norsk institutt for Bioøkonomi; Leibniz-Gemeinschaft; Directorate for Biological Sciences; Masarykova Univerzita; Eötvös Loránd Tudományegyetem; Akademie Věd České Republiky; Friedrich-Schiller-Universität Jena; University of Nottingham; Stockholms Universitet; Purdue University; University of Oxford; Universiteit Gent; Uniwersytet Warszawski; Université de Sherbrooke; Sveriges Lantbruksuniversitet; Uniwersytet Rzeszowski; University of Wisconsin-Madison; Universität Potsdam; Trinity College Dublin","keywords":"Representativeness heuristic; Biome; Orthogonality; Range (aeronautics); Geography; Computer science; Ecology; Statistics; Mathematics; Ecosystem; Biology; Engineering","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":[0.05016149,0.0009522412,0.001490824,0.008715309,0.001495298,0.002044183,0.002874312,0.0009599275,0.00348915],"category_scores_gemma":[0.07174205,0.000876182,0.001390402,0.007894393,0.0008241169,0.004313617,0.00592005,0.001168208,0.001398837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001389796,"about_ca_system_score_gemma":0.001645038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01420591,"about_ca_topic_score_gemma":0.03499683,"domain_scores_codex":[0.9599527,0.02906593,0.003245374,0.003299046,0.003405413,0.001031628],"domain_scores_gemma":[0.8842711,0.0250428,0.01741913,0.04837955,0.02202947,0.002857865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001212752,0.001326879,0.6489999,0.001397548,0.002219981,0.0003557822,0.01100461,0.009309873,0.01464289,0.004080714,0.02354149,0.2819076],"study_design_scores_gemma":[0.0001524908,0.00130476,0.9005827,0.0003527235,0.0005390465,0.000338385,0.009250538,0.01174965,0.009809772,0.00587106,0.05978267,0.0002663053],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6107061,0.001231032,0.3273886,0.001457016,0.0002807624,0.006784325,0.03526206,0.003163547,0.01372655],"genre_scores_gemma":[0.6084021,0.0005454442,0.3369865,0.0008842038,0.0001233956,0.01250513,0.03746162,0.0007754381,0.002316258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05016149,"threshold_uncertainty_score":0.2652825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.207528734189244,"score_gpt":0.3903315738303909,"score_spread":0.1828028396411469,"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."}}