{"id":"W6925066850","doi":"10.15468/dl.zbtzfh","title":"Occurrence Download","year":2019,"lang":"en","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); Range (aeronautics); Data modeling; Process (computing)","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":[0.000981587,0.001940537,0.001630844,0.00502368,0.001065229,0.002986643,0.002988544,0.001981519,0.1853654],"category_scores_gemma":[0.006459807,0.0009603597,0.001274752,0.01037732,0.0004646661,0.002499123,0.002875154,0.002160909,0.2587652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001730794,"about_ca_system_score_gemma":0.002643122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02590119,"about_ca_topic_score_gemma":0.04193223,"domain_scores_codex":[0.9989616,0.0001348381,0.0001355856,0.0003687013,0.0002283335,0.0001709989],"domain_scores_gemma":[0.9976127,0.0006816998,0.0002160034,0.000615829,0.0006020084,0.0002718125],"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":[0.00002486048,0.000009523375,0.0003321933,0.0005164496,0.00001381869,0.000013697,0.00002394891,0.0001006959,0.00009126495,0.0003944092,0.9970677,0.001411498],"study_design_scores_gemma":[0.00005627259,0.000006169608,0.001467528,0.0001922245,0.00001326618,0.00003315479,0.00006840329,0.0001258391,0.0001569248,0.0007945547,0.99707,0.00001577675],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000318319,0.00002842681,0.00004656506,0.00003830692,0.0000129744,0.000005316413,0.9987009,0.0004303669,0.0007053421],"genre_scores_gemma":[0.0001476799,0.0000411644,0.0002178951,0.00005066221,0.000003685875,0.00004659883,0.9987105,0.0001962076,0.00058555],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8146346,"threshold_uncertainty_score":0.620109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0225412765836933,"score_gpt":0.2352696232258381,"score_spread":0.2127283466421448,"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."}}