{"id":"W6924628261","doi":"10.15468/dl.v9xstx","title":"Occurrence Download","year":2022,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Range (aeronautics); Download; Set (abstract data type); Identification (biology)","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":[0.0009762439,0.001954631,0.001566547,0.004984939,0.0008758929,0.00248409,0.002705771,0.001839331,0.1472655],"category_scores_gemma":[0.005756763,0.000917164,0.001126411,0.009327397,0.0004233604,0.002102923,0.002346958,0.001912934,0.2010213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00148993,"about_ca_system_score_gemma":0.002361353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01740611,"about_ca_topic_score_gemma":0.03046019,"domain_scores_codex":[0.9989486,0.0001454537,0.0001443675,0.0003716445,0.0002233267,0.0001666408],"domain_scores_gemma":[0.9976043,0.0006771989,0.0002423714,0.0005883846,0.0006037227,0.0002839683],"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":[0.00003434463,0.00001242855,0.0003943029,0.0005711569,0.00001668172,0.0000148124,0.00002033096,0.0001290557,0.000115401,0.000414092,0.9969008,0.001376686],"study_design_scores_gemma":[0.00008673232,0.000008447542,0.001784228,0.0001757904,0.00001588886,0.00003468054,0.00005652678,0.0001621761,0.0002009602,0.0009043047,0.9965519,0.00001843759],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003749566,0.00002259886,0.00004036131,0.00002863809,0.000009433886,0.000005352993,0.9989532,0.0003214316,0.0005814268],"genre_scores_gemma":[0.0001604517,0.00003332591,0.0002025816,0.00004750427,0.000003464736,0.00004548509,0.9988964,0.0001469141,0.000463886],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8527344,"threshold_uncertainty_score":0.4926523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06850837321744672,"score_gpt":0.304165618038287,"score_spread":0.2356572448208403,"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."}}