{"id":"W6943306832","doi":"10.15468/dl.jzep8w","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); Data set; State (computer science)","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":[0.001131541,0.002020179,0.00166798,0.005189336,0.001083225,0.002760087,0.003001317,0.002056328,0.1788628],"category_scores_gemma":[0.006500385,0.0009909341,0.001145933,0.01024481,0.0004704162,0.002426965,0.002674656,0.002123785,0.2437589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001730969,"about_ca_system_score_gemma":0.002586172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02360168,"about_ca_topic_score_gemma":0.03806576,"domain_scores_codex":[0.9988888,0.0001462788,0.0001385725,0.0003845313,0.0002583939,0.0001834471],"domain_scores_gemma":[0.9972843,0.0007779266,0.000255897,0.0007047287,0.0006723407,0.0003048975],"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":[0.00002658572,0.00001016744,0.0003198446,0.0004957914,0.00001396556,0.00001408689,0.00002414748,0.0001090577,0.0001114652,0.0003958917,0.9971705,0.001308431],"study_design_scores_gemma":[0.00006310411,0.000006473809,0.00157682,0.0001759698,0.00001336079,0.0000330109,0.00006698885,0.0001229661,0.0001849494,0.0007644281,0.9969742,0.00001760543],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003338736,0.00002258288,0.00004874976,0.00003241908,0.00001080792,0.000004750012,0.9987943,0.0003898194,0.0006632064],"genre_scores_gemma":[0.0001444078,0.00003057698,0.0002040386,0.00004106926,0.000003117273,0.00004137369,0.9988645,0.0001874794,0.0004833164],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8211372,"threshold_uncertainty_score":0.5983557,"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."}}