{"id":"W6887077380","doi":"10.15468/dl.m2amje","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":[0.0009352518,0.002129549,0.001376903,0.004420986,0.001013112,0.002317113,0.002889968,0.001941125,0.1136352],"category_scores_gemma":[0.006063695,0.0008476837,0.001250012,0.008586874,0.000461442,0.002187472,0.002470534,0.001994686,0.17586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001641299,"about_ca_system_score_gemma":0.002464213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02450899,"about_ca_topic_score_gemma":0.04219208,"domain_scores_codex":[0.9989011,0.000146662,0.0001489564,0.0003747161,0.000260135,0.0001683073],"domain_scores_gemma":[0.9975689,0.000639917,0.0002174479,0.0006617254,0.0006377995,0.0002742073],"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":[0.00003019707,0.00001313215,0.0003604805,0.0004022319,0.00001240151,0.00001457711,0.00002125115,0.000126858,0.0001069024,0.000372686,0.9971343,0.001404801],"study_design_scores_gemma":[0.00008646176,0.00001072812,0.001814895,0.0001559369,0.00001327665,0.00004640047,0.00007722029,0.0002419456,0.0002254672,0.0008864756,0.9964226,0.00001863485],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006798402,0.00003272992,0.00006366591,0.00005006683,0.00001596109,0.000007605042,0.9982947,0.000676284,0.0007910972],"genre_scores_gemma":[0.0001747647,0.00003252296,0.0002602338,0.00004861702,0.000003615131,0.00004150724,0.9988294,0.0001579571,0.0004512229],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8863648,"threshold_uncertainty_score":0.3801476,"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."}}