{"id":"W6905583328","doi":"10.15468/dl.9f4h29","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":[0.0009231116,0.002158684,0.001338476,0.004383573,0.001025056,0.002267093,0.002882513,0.00193621,0.1004293],"category_scores_gemma":[0.005628501,0.0008377607,0.001258626,0.008402129,0.0004540856,0.00212112,0.002362243,0.00196662,0.1636184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001638716,"about_ca_system_score_gemma":0.002525546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02487626,"about_ca_topic_score_gemma":0.04339346,"domain_scores_codex":[0.998909,0.000143421,0.0001503472,0.0003727102,0.000255444,0.0001690873],"domain_scores_gemma":[0.9976921,0.0005793953,0.0002113972,0.0006301684,0.000615844,0.0002711721],"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":[0.00003260838,0.00001498354,0.000405802,0.0003998718,0.00001290433,0.00001552461,0.00002176502,0.0001318146,0.0001205031,0.0003852317,0.9969702,0.001488828],"study_design_scores_gemma":[0.00008831616,0.00001180188,0.002087652,0.0001554344,0.00001400365,0.00004881427,0.00007987645,0.0002530082,0.0002465115,0.0008644537,0.9961305,0.00001950792],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007724077,0.00003379027,0.00006271886,0.00004834439,0.00001574913,0.000007935271,0.9983145,0.0006303403,0.0008094969],"genre_scores_gemma":[0.0001719096,0.00002980293,0.0002450924,0.00004355352,0.000003343381,0.0000373246,0.9989117,0.0001280876,0.0004292348],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8995707,"threshold_uncertainty_score":0.3359695,"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."}}