{"id":"W6887212956","doi":"10.15468/dl.utj7cm","title":"Occurrence Download","year":2022,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Download; Alien; Range (aeronautics); State (computer science)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00096074,0.001986945,0.00145029,0.004700408,0.0008956945,0.002594779,0.002607279,0.002049141,0.1497696],"category_scores_gemma":[0.006264186,0.0007864847,0.001155667,0.009297078,0.0004325096,0.001886063,0.002257729,0.001928702,0.2230101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001515325,"about_ca_system_score_gemma":0.002241504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01992297,"about_ca_topic_score_gemma":0.03280633,"domain_scores_codex":[0.9990059,0.0001510055,0.0001190431,0.0003487222,0.000215004,0.0001603431],"domain_scores_gemma":[0.9976489,0.0007157158,0.0002306016,0.0005880041,0.0005667474,0.0002501305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002635923,0.0000113712,0.0004042091,0.0004196295,0.00001234921,0.00001270065,0.00001747835,0.0001440477,0.00007463879,0.0004036684,0.997076,0.001397447],"study_design_scores_gemma":[0.00008174891,0.000009295883,0.00188221,0.0001836877,0.00001332199,0.00003332204,0.00006061122,0.0002129244,0.0001527922,0.001080432,0.9962729,0.00001676238],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004926392,0.00003170318,0.00004760442,0.00004050821,0.00001198233,0.000005458818,0.9987556,0.0003809837,0.0006769205],"genre_scores_gemma":[0.0001828311,0.00004042014,0.0002190548,0.00005003164,0.000004422907,0.00004941331,0.9987239,0.0001444022,0.0005854356],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8502303,"threshold_uncertainty_score":0.5010294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01988484771547973,"score_gpt":0.2049971774180899,"score_spread":0.1851123297026102,"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."}}