{"id":"W6962311319","doi":"10.15468/dl.ccvx34","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":[0.0009075025,0.002147056,0.001346133,0.004421962,0.001000615,0.002287335,0.002852299,0.001874371,0.108746],"category_scores_gemma":[0.005702761,0.0008336849,0.001260725,0.008327954,0.0004475407,0.002177428,0.002352441,0.001951275,0.1732635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001620097,"about_ca_system_score_gemma":0.002432874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0244822,"about_ca_topic_score_gemma":0.0423027,"domain_scores_codex":[0.9989151,0.0001427878,0.0001465762,0.0003756361,0.0002555655,0.0001644228],"domain_scores_gemma":[0.9976918,0.0005940771,0.0002057476,0.0006372897,0.0006105007,0.000260703],"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":[0.0000299205,0.00001338614,0.0003732981,0.0003749023,0.00001227337,0.00001489669,0.00002041896,0.0001278622,0.0001137279,0.0003736643,0.9970841,0.001461469],"study_design_scores_gemma":[0.00008028311,0.00001041855,0.001839349,0.0001455533,0.00001304386,0.00004838905,0.00007352326,0.000250039,0.0002361932,0.0008746234,0.9964105,0.00001811721],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000722453,0.00003388873,0.00006723594,0.00004984048,0.00001594536,0.000007682345,0.9982021,0.0007089384,0.0008421056],"genre_scores_gemma":[0.0001750897,0.00003169448,0.0002587728,0.00004918234,0.000003592549,0.00003820164,0.9988309,0.0001601491,0.0004524816],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.891254,"threshold_uncertainty_score":0.3637915,"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."}}