{"id":"W6943292779","doi":"10.15468/dl.v38aeu","title":"Occurrence Download","year":2025,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Range (aeronautics); Set (abstract data type); Identification (biology); Download","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":[0.0009315379,0.002165667,0.001386551,0.004551909,0.0009963665,0.002321295,0.002825839,0.001920726,0.1107138],"category_scores_gemma":[0.006205293,0.0008446484,0.00127695,0.008472808,0.0004521932,0.002176838,0.002390828,0.001961492,0.1712916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001642286,"about_ca_system_score_gemma":0.002440915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02423138,"about_ca_topic_score_gemma":0.04063402,"domain_scores_codex":[0.998882,0.0001494348,0.0001539163,0.0003826021,0.0002644877,0.0001675097],"domain_scores_gemma":[0.9975556,0.0006493728,0.0002185081,0.0006679697,0.0006405066,0.0002680434],"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":[0.00003119515,0.00001323261,0.000365659,0.0004071666,0.0000129788,0.00001485883,0.00002074153,0.0001334282,0.0001094058,0.0003634696,0.99705,0.001477821],"study_design_scores_gemma":[0.00008769729,0.00001115261,0.001876495,0.0001582327,0.00001393865,0.00004844075,0.00007611357,0.000268649,0.0002297981,0.0008814501,0.9963295,0.00001848403],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007127495,0.00003552494,0.00006602897,0.00005248403,0.00001640411,0.000007953171,0.9982619,0.0007056157,0.0007828753],"genre_scores_gemma":[0.0001828878,0.00003412325,0.0002647713,0.0000500838,0.000003761712,0.00004180167,0.9988111,0.0001609419,0.0004505024],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8892862,"threshold_uncertainty_score":0.3703747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03761864349018661,"score_gpt":0.2888078212483628,"score_spread":0.2511891777581762,"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."}}