{"id":"W7083690105","doi":"10.15468/dl.a8e9cj","title":"Occurrence Download","year":2025,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Download; Matching (statistics); Range (aeronautics); Data set; Identification (biology)","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":[0.0009618977,0.001846533,0.001455071,0.004687252,0.0008850283,0.002469622,0.002744157,0.002116783,0.1314491],"category_scores_gemma":[0.0057472,0.0007841988,0.001105918,0.00871376,0.0004279634,0.002077366,0.002254354,0.001906961,0.1980924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001660798,"about_ca_system_score_gemma":0.002215995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02216356,"about_ca_topic_score_gemma":0.03844368,"domain_scores_codex":[0.998934,0.0001651269,0.0001341377,0.000358243,0.0002340336,0.0001745408],"domain_scores_gemma":[0.9976391,0.0006233625,0.0002329129,0.000621132,0.0006139107,0.000269709],"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":[0.00002866103,0.00001051653,0.0003461955,0.0004144093,0.00001344569,0.00001392798,0.00001687816,0.0001241995,0.00007746217,0.0003981753,0.9973679,0.001188169],"study_design_scores_gemma":[0.00008670344,0.000008650583,0.001852891,0.0001726379,0.00001359992,0.00003669854,0.00005802709,0.0001883967,0.0001585806,0.0009735068,0.9964339,0.00001647252],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004550726,0.0000292758,0.00004078175,0.00004004173,0.00001064933,0.000005303947,0.998865,0.0003481316,0.0006153763],"genre_scores_gemma":[0.0001750871,0.00003394405,0.0001710393,0.00005404968,0.000003720582,0.00003670297,0.9989147,0.0001185382,0.0004921776],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8685508,"threshold_uncertainty_score":0.4397412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01875642842837222,"score_gpt":0.1956212581659553,"score_spread":0.1768648297375831,"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."}}