{"id":"W6887019060","doi":"10.15468/dl.k3un3x","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); Alien; 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":[0.0009581051,0.001958801,0.001544995,0.004638701,0.001064136,0.002794439,0.002874549,0.002166409,0.175767],"category_scores_gemma":[0.006115969,0.0009251614,0.001243215,0.008702038,0.0004319246,0.002715345,0.002647534,0.002125906,0.2479507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001625249,"about_ca_system_score_gemma":0.002290251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01983974,"about_ca_topic_score_gemma":0.03354809,"domain_scores_codex":[0.9989593,0.0001497035,0.0001230155,0.0003804347,0.0002230354,0.0001644899],"domain_scores_gemma":[0.9975672,0.0007041643,0.0002009939,0.0006610046,0.0006028372,0.000263847],"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":[0.00002553449,0.00001085385,0.0003183513,0.0004602719,0.0000121467,0.00001345271,0.00001977981,0.0001057633,0.00009099293,0.0003571786,0.997238,0.001347754],"study_design_scores_gemma":[0.00006427861,0.000008021157,0.001537441,0.0001808378,0.00001236085,0.00003484541,0.00006878054,0.0001565821,0.0001653166,0.000853396,0.9969008,0.00001731825],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003967862,0.00002913517,0.00004994591,0.00004522676,0.00001416545,0.000006173455,0.9986466,0.0004318176,0.0007371767],"genre_scores_gemma":[0.0001595965,0.00003763819,0.0002182277,0.00005727477,0.000004251384,0.00004884578,0.9987165,0.0001723933,0.0005853358],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.824233,"threshold_uncertainty_score":0.5879992,"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."}}