{"id":"W6924507442","doi":"10.15468/dl.2r4jt2","title":"Occurrence Download","year":2025,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Range (aeronautics); Set (abstract data type); Identification (biology); Download","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":[0.0009431478,0.002122195,0.001512615,0.004320551,0.0009849811,0.002341326,0.002925768,0.001831027,0.1262238],"category_scores_gemma":[0.005809581,0.0008527157,0.001266592,0.007892989,0.0004484218,0.002014583,0.002323163,0.001881145,0.1826077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001526791,"about_ca_system_score_gemma":0.00237826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02154871,"about_ca_topic_score_gemma":0.03784141,"domain_scores_codex":[0.9989659,0.0001412391,0.0001338264,0.0003771224,0.0002284833,0.0001534078],"domain_scores_gemma":[0.9977524,0.0006314894,0.0002110816,0.0005780158,0.0005768897,0.0002501536],"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":[0.00003171636,0.00001225689,0.0003784712,0.0004476628,0.00001426507,0.00001446938,0.0000214255,0.0001347732,0.0001108581,0.0003562446,0.9969938,0.001484074],"study_design_scores_gemma":[0.00008540118,0.00001100567,0.001847046,0.0001586207,0.00001617764,0.0000462486,0.00007090873,0.0002401288,0.0002354001,0.0009309263,0.9963381,0.00002005024],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000633882,0.00003183564,0.00006860196,0.00004111989,0.00001405916,0.000007060194,0.9983947,0.0006671711,0.0007120582],"genre_scores_gemma":[0.0001904995,0.00003217415,0.0002916527,0.00005001761,0.000003635426,0.00004801393,0.9987019,0.0001943977,0.000487556],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8737762,"threshold_uncertainty_score":0.4222608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01112378987140698,"score_gpt":0.2222484648505405,"score_spread":0.2111246749791335,"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."}}