{"id":"W7105905776","doi":"10.15468/dl.57eusx","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":[0.0008839837,0.001894519,0.001633022,0.005204021,0.001186603,0.002786327,0.002867737,0.002217128,0.1533195],"category_scores_gemma":[0.006550619,0.0008942884,0.001292193,0.01003796,0.0004209522,0.002602493,0.002593387,0.00200198,0.2118177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001930775,"about_ca_system_score_gemma":0.002813522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02796187,"about_ca_topic_score_gemma":0.04688558,"domain_scores_codex":[0.9988294,0.0001431513,0.0001510915,0.0004177667,0.0002591989,0.0001993461],"domain_scores_gemma":[0.9972932,0.0007299055,0.0002409237,0.0006993347,0.0007415165,0.0002951459],"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":[0.00003494223,0.00001189251,0.0004779939,0.0005740293,0.00001479046,0.00001749263,0.00002301203,0.0001248923,0.0001190338,0.0003938575,0.9964373,0.00177077],"study_design_scores_gemma":[0.00006826566,0.000009165423,0.002011883,0.0002042202,0.00001403796,0.00004274348,0.00008674814,0.000184869,0.0001950841,0.000755449,0.9964096,0.00001800441],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004862347,0.00002984968,0.00004775741,0.00004020143,0.00001375815,0.000005360452,0.9987391,0.0004444314,0.0006310406],"genre_scores_gemma":[0.0001714602,0.00003559451,0.0002202628,0.00004567636,0.000003865411,0.00003867459,0.9988366,0.0001391438,0.0005087717],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8466805,"threshold_uncertainty_score":0.5129048,"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."}}