{"id":"W7105887571","doi":"10.15468/dl.7qhgbp","title":"Occurrence Download","year":2025,"lang":"","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Download; Matching (statistics); Herbarium; Polygon (computer graphics); Data collection","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":[0.0008838612,0.0018787,0.001623864,0.005342823,0.001186932,0.002805778,0.002824475,0.002190016,0.1578577],"category_scores_gemma":[0.006651792,0.0008924578,0.001297316,0.01022797,0.00041442,0.002612731,0.002596408,0.001965829,0.2163342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001923919,"about_ca_system_score_gemma":0.002801805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02842072,"about_ca_topic_score_gemma":0.04733484,"domain_scores_codex":[0.9988219,0.0001436732,0.0001535951,0.0004189431,0.0002621705,0.0001997113],"domain_scores_gemma":[0.9972405,0.0007501647,0.000244918,0.0007043813,0.0007662027,0.0002938262],"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":[0.00003539622,0.00001180664,0.0004893036,0.0005892612,0.00001503834,0.00001797674,0.00002404092,0.0001241811,0.0001209747,0.0003966142,0.9963567,0.001818707],"study_design_scores_gemma":[0.00006583898,0.000008930184,0.002004669,0.000204355,0.0000139951,0.00004216183,0.00008741258,0.0001791058,0.0001921761,0.000735919,0.9964476,0.00001780775],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004811007,0.00002929248,0.00004790363,0.00003915661,0.00001339265,0.000005263691,0.9987252,0.0004514334,0.000640204],"genre_scores_gemma":[0.0001718063,0.00003564636,0.000221047,0.00004521003,0.000003838827,0.00003860231,0.9988171,0.0001463738,0.0005203898],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8421423,"threshold_uncertainty_score":0.5280865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01434536157882973,"score_gpt":0.2311093459842124,"score_spread":0.2167639844053826,"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."}}