{"id":"W7105974694","doi":"10.15468/dl.7w9kaq","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":[0.0008839378,0.001886542,0.001627046,0.00532424,0.001187577,0.002810824,0.002828543,0.002194124,0.1579807],"category_scores_gemma":[0.006635139,0.0008947342,0.001302328,0.01018955,0.0004143262,0.002619931,0.002597323,0.001971674,0.2164219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001920668,"about_ca_system_score_gemma":0.002792218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02835825,"about_ca_topic_score_gemma":0.04714458,"domain_scores_codex":[0.9988237,0.0001435059,0.0001529122,0.0004189067,0.0002616018,0.0001993923],"domain_scores_gemma":[0.9972486,0.000749688,0.0002431725,0.000702729,0.0007627709,0.0002930075],"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":[0.00003551303,0.00001186551,0.0004876198,0.0005898639,0.00001508184,0.00001807496,0.00002404655,0.0001246071,0.0001215876,0.0003976516,0.9963562,0.001817953],"study_design_scores_gemma":[0.00006618437,0.000008970228,0.001999846,0.0002040513,0.00001405307,0.0000423093,0.00008723116,0.0001803862,0.0001934561,0.0007406232,0.9964451,0.00001786527],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004822015,0.00002933435,0.00004822171,0.0000393513,0.00001343721,0.00000528484,0.9987181,0.0004562574,0.000641754],"genre_scores_gemma":[0.0001711434,0.00003555918,0.0002209844,0.00004529039,0.000003831735,0.00003849571,0.9988192,0.0001470742,0.0005183718],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8420193,"threshold_uncertainty_score":0.5284979,"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."}}