{"id":"W6962509103","doi":"10.15468/dl.ncnz9x","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":[0.000927233,0.002130696,0.001385552,0.004517382,0.0009780891,0.002320501,0.002792857,0.001877132,0.1192716],"category_scores_gemma":[0.006260417,0.0008763282,0.001248538,0.008401453,0.0004431781,0.002228057,0.002473748,0.001957369,0.1757988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001652632,"about_ca_system_score_gemma":0.002402933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02383247,"about_ca_topic_score_gemma":0.03921079,"domain_scores_codex":[0.9989304,0.0001368173,0.0001495992,0.0003681469,0.000251051,0.0001640893],"domain_scores_gemma":[0.9975426,0.0006517834,0.0002244002,0.0006737169,0.0006314705,0.0002761138],"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":[0.00003077339,0.0000128178,0.0003621544,0.0004110473,0.0000125112,0.00001466204,0.00002159855,0.00012866,0.0001130069,0.0003733906,0.9970446,0.00147474],"study_design_scores_gemma":[0.00008769403,0.00001045265,0.001874785,0.0001588897,0.00001343794,0.00004683603,0.00007386232,0.0002582771,0.0002291689,0.0008835212,0.9963446,0.00001839087],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006608515,0.00003101496,0.00006596004,0.00004952783,0.00001523281,0.000007896322,0.9982693,0.0007229376,0.0007720149],"genre_scores_gemma":[0.0001829646,0.00003321154,0.0002729906,0.00005191581,0.000003699203,0.00004465289,0.998773,0.0001831166,0.0004543369],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8807284,"threshold_uncertainty_score":0.3990031,"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."}}