{"id":"W6887142058","doi":"10.15468/dl.nqssdb","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":[0.0009245612,0.002076344,0.00135004,0.004464631,0.000997474,0.002297721,0.002826252,0.001862972,0.1162437],"category_scores_gemma":[0.006021521,0.0008378736,0.001216989,0.008536195,0.0004483197,0.002169071,0.002394608,0.001912317,0.1758024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001643728,"about_ca_system_score_gemma":0.00250501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02511529,"about_ca_topic_score_gemma":0.04326609,"domain_scores_codex":[0.9989192,0.0001439442,0.0001473657,0.0003688351,0.0002557255,0.0001647659],"domain_scores_gemma":[0.9975866,0.0006392171,0.0002179819,0.0006475571,0.0006383679,0.0002702308],"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":[0.0000293569,0.000012407,0.0003578405,0.0003887253,0.00001188258,0.00001399529,0.00002055615,0.0001208888,0.0001023444,0.0003709108,0.9971561,0.00141499],"study_design_scores_gemma":[0.00008151225,0.00001011976,0.001802738,0.0001515877,0.00001279931,0.00004421237,0.00007313277,0.0002248163,0.0002139367,0.0008598964,0.9965076,0.00001759494],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006606711,0.0000318954,0.00006259728,0.00004851099,0.00001496239,0.000007419011,0.9983053,0.0006461726,0.0008170207],"genre_scores_gemma":[0.0001760111,0.00003237684,0.0002518883,0.00004905247,0.000003579273,0.00004082338,0.9988153,0.0001600509,0.0004710322],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8837563,"threshold_uncertainty_score":0.3888741,"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."}}