{"id":"W2604718223","doi":"10.1111/2041-210x.12779","title":"A method for the objective selection of landscape‐scale study regions and sites at the national level","year":2017,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; Department for Environment, Food and Rural Affairs, UK Government; Scottish Government; Sight Research UK; Wellcome Trust; Natural Environment Research Council; McGill University; Directorate for Biological Sciences; Wellcome","keywords":"Representativeness heuristic; Selection (genetic algorithm); Comparability; Scale (ratio); Site selection; Metric (unit); Variable (mathematics); Geography; Multivariate statistics; Abundance (ecology); Field (mathematics); Land cover; Ecology; Computer science; Environmental resource management; Statistics; Cartography; Land use; Environmental science; Biology; Mathematics; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02221033,0.001032819,0.00129751,0.003706397,0.001364361,0.002227314,0.002071452,0.001082304,0.01265519],"category_scores_gemma":[0.05055096,0.0009918219,0.001363407,0.003457134,0.001398775,0.0009687902,0.003123547,0.002094727,0.003479994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001080006,"about_ca_system_score_gemma":0.005199434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003940642,"about_ca_topic_score_gemma":0.009734923,"domain_scores_codex":[0.9864059,0.007005906,0.0016057,0.002622556,0.001988229,0.0003717741],"domain_scores_gemma":[0.9638248,0.01977773,0.003191922,0.006432628,0.006107821,0.0006650653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001249696,0.0006633824,0.01975567,0.001740369,0.0006051969,0.0003708707,0.002585028,0.01297222,0.0502794,0.03949446,0.05061834,0.8196655],"study_design_scores_gemma":[0.00413939,0.001014879,0.07672647,0.0007390669,0.0007533333,0.0009957509,0.002210511,0.5464879,0.05127051,0.1005495,0.2144685,0.0006443355],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008254119,0.0000371383,0.9831051,0.0001252358,0.00007650072,0.003220856,0.001102797,0.002923602,0.001154566],"genre_scores_gemma":[0.01411349,0.00001266729,0.978754,0.00006833858,0.000015773,0.005530666,0.0006449548,0.0002382627,0.0006217882],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02221033,"threshold_uncertainty_score":0.1174608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1463145724747713,"score_gpt":0.3815634839842234,"score_spread":0.2352489115094521,"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."}}