{"id":"W4400915369","doi":"10.1111/ecog.07061","title":"KBAscope: key biodiversity area identification in R","year":2024,"lang":"en","type":"article","venue":"Ecography","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Research Executive Agency; European Commission","keywords":"Biodiversity; Geography; Identification (biology); Occupancy; Habitat; Environmental resource management; Global biodiversity; Taxon; Ecology; Population; Taxonomic rank; Scope (computer science); Range (aeronautics); Key (lock); Environmental science; Computer science; Biology; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001116323,0.00005076526,0.00004064075,0.0000743538,0.00004566351,0.00005944628,0.00009475959,0.00002864181,0.05321142],"category_scores_gemma":[0.000004965565,0.00004967724,0.00005401135,0.0006683866,0.00007010825,0.0001540444,0.00005083066,0.00005271924,0.007469026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001155471,"about_ca_system_score_gemma":0.000001601057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001789012,"about_ca_topic_score_gemma":0.0005273978,"domain_scores_codex":[0.9995043,0.00001169751,0.00007924685,0.000180511,0.0001078338,0.0001164293],"domain_scores_gemma":[0.9998419,0.000009112237,0.00001170148,0.00009982071,0.000002047966,0.0000354369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006290277,0.00009075231,0.7616722,0.00001617927,0.000009363232,0.0000228953,0.0005785773,0.000007827563,0.003683227,0.0007879829,0.2288243,0.00430047],"study_design_scores_gemma":[0.00006247814,0.000007123773,0.8592029,0.000005743605,0.000004178293,0.000001011788,0.0003613021,0.00005828823,0.000647996,0.0001897726,0.1393843,0.00007488942],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9618797,0.000171458,0.000043653,0.0006421072,0.0002579479,0.00007303603,0.00007975268,0.0000775834,0.03677475],"genre_scores_gemma":[0.9994889,0.0001164609,0.000009599842,0.0001115881,0.000006100776,0.000006724182,0.00007089306,0.000001947976,0.0001878068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09753074,"threshold_uncertainty_score":0.9933038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02033186611946598,"score_gpt":0.2241109834530039,"score_spread":0.2037791173335379,"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."}}