{"id":"W6905655305","doi":"10.15468/dl.pcb958","title":"Occurrence Download","year":2021,"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); China; Feature (linguistics)","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":[0.0007796997,0.003032977,0.002192389,0.007551946,0.001206351,0.003434109,0.002393364,0.002537818,0.2335656],"category_scores_gemma":[0.005968583,0.00098807,0.002102975,0.01049618,0.000412193,0.003454466,0.003493856,0.002095957,0.2898338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001365534,"about_ca_system_score_gemma":0.002894893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02299944,"about_ca_topic_score_gemma":0.03758366,"domain_scores_codex":[0.9987785,0.0001417048,0.0001901935,0.0004187801,0.0002610126,0.0002098067],"domain_scores_gemma":[0.9976377,0.0007073625,0.0001922978,0.0005688774,0.000561927,0.0003317859],"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":[0.00006688595,0.00002229562,0.0004523853,0.0009823843,0.00002327883,0.00003621934,0.00004115854,0.0002241227,0.0001542734,0.0005865601,0.993008,0.004402281],"study_design_scores_gemma":[0.00009503335,0.00001605481,0.001428259,0.0002532304,0.00002259607,0.00006968501,0.0001054438,0.0004514868,0.0002084788,0.001392341,0.9959288,0.00002861089],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001071555,0.000115865,0.0001610456,0.00007549249,0.00004333044,0.0000167777,0.9950453,0.002314184,0.002120779],"genre_scores_gemma":[0.0003304998,0.0001265826,0.0007232769,0.0001081895,0.00001109783,0.00006367772,0.9969612,0.0004585609,0.001216902],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7664344,"threshold_uncertainty_score":0.7813548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01878725496536469,"score_gpt":0.2292776369319846,"score_spread":0.2104903819666199,"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."}}