{"id":"W6906032006","doi":"10.15468/dl.rfsgbb","title":"Occurrence Download","year":2022,"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); Identification (biology); Real world data","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":[0.00093917,0.002131048,0.001609864,0.004965222,0.001090318,0.002657473,0.003022657,0.002131399,0.1544987],"category_scores_gemma":[0.005866195,0.0009434848,0.001175529,0.009758191,0.0004859637,0.002412826,0.00271117,0.002078242,0.227957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001735624,"about_ca_system_score_gemma":0.002469369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02636947,"about_ca_topic_score_gemma":0.04475517,"domain_scores_codex":[0.9990063,0.000126851,0.0001165268,0.000342523,0.0002366408,0.000171227],"domain_scores_gemma":[0.9978518,0.0005922444,0.000180081,0.0005578855,0.0005740832,0.0002438518],"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":[0.00002672651,0.00001168159,0.0003564012,0.0005239017,0.00001362985,0.0000163474,0.00002712549,0.0001265933,0.0001239085,0.0003842571,0.9968626,0.00152689],"study_design_scores_gemma":[0.00006589049,0.000007549927,0.001571955,0.0001881976,0.00001326276,0.0000413327,0.00009045783,0.0001699585,0.0002139474,0.0007532947,0.9968658,0.00001843394],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004721888,0.00003110536,0.0000557886,0.00003719311,0.00001311106,0.000005849325,0.9985435,0.0005279316,0.0007382389],"genre_scores_gemma":[0.0001688175,0.00003440064,0.0002376451,0.00004074532,0.000003195952,0.00004276536,0.9988201,0.0001778228,0.0004744218],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8455013,"threshold_uncertainty_score":0.5168495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01850871640220096,"score_gpt":0.2277224731265546,"score_spread":0.2092137567243537,"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."}}