{"id":"W6924692244","doi":"10.15468/dl.vuqvfe","title":"Occurrence Download","year":2023,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Download; Matching (statistics); Range (aeronautics); Alien; Data access","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001930673,0.0002091757,0.0001837392,0.00009803885,0.0003064392,0.0003542718,0.00134493,0.0001561175,0.00005964059],"category_scores_gemma":[0.0000486338,0.0002282455,0.00013443,0.000870807,0.00006884742,0.00110472,0.0008136891,0.0002029395,0.5636329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001674853,"about_ca_system_score_gemma":0.0002073552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004078078,"about_ca_topic_score_gemma":0.000008665353,"domain_scores_codex":[0.9986227,0.000036708,0.0003141159,0.0002800648,0.000518419,0.0002280547],"domain_scores_gemma":[0.9985061,0.00005170317,0.0002465298,0.0007527048,0.0003052671,0.0001377415],"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":[0.000002403657,0.00002262874,0.00002196373,0.00003880916,0.00001538851,8.438518e-7,0.00002094975,0.00009718365,1.720496e-8,0.00002373184,0.9965875,0.003168543],"study_design_scores_gemma":[0.0001263042,0.0000131295,0.0001166496,0.000001327751,0.00001041222,0.00000190991,0.00001003404,0.00001810879,5.597754e-7,0.000112677,0.9993597,0.0002292177],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001281482,0.000004760433,0.002781312,0.0006098393,0.0005555063,0.0002424875,0.9954355,0.0002874447,0.00007032382],"genre_scores_gemma":[0.000004108306,0.00002320176,0.00002269006,0.0004833216,0.000002051976,0.000009346857,0.9994551,1.09806e-8,1.527657e-7],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5635732,"threshold_uncertainty_score":0.9307579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01919621017890426,"score_gpt":0.2370784155883494,"score_spread":0.2178822054094451,"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."}}