{"id":"W6924885109","doi":"10.15468/dl.zc26ne","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":[0.0008735328,0.002011327,0.001260756,0.004273099,0.0009602024,0.002139725,0.002718903,0.00182292,0.1047783],"category_scores_gemma":[0.005377557,0.0007868786,0.0011929,0.007901896,0.0004295865,0.002034426,0.002308621,0.001828808,0.1635878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001550327,"about_ca_system_score_gemma":0.00237092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02392127,"about_ca_topic_score_gemma":0.04245667,"domain_scores_codex":[0.9989926,0.0001307874,0.0001381704,0.0003435266,0.0002370242,0.0001578834],"domain_scores_gemma":[0.9978492,0.000527772,0.0001972892,0.0005939159,0.0005822836,0.0002495026],"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":[0.00003059574,0.00001362743,0.0004001221,0.0003958709,0.00001224958,0.00001517563,0.00002185167,0.0001270966,0.0001194915,0.0003774826,0.9969421,0.001544259],"study_design_scores_gemma":[0.00007791916,0.00001062134,0.002000327,0.0001507155,0.00001301072,0.00004707086,0.00007658088,0.0002395928,0.0002396077,0.00082126,0.996305,0.00001825991],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000764865,0.00003287755,0.000064752,0.00004729697,0.00001612982,0.000007945032,0.9982727,0.0006455663,0.0008362719],"genre_scores_gemma":[0.0001849334,0.00003018232,0.0002650668,0.00004566082,0.000003496524,0.00003938834,0.99883,0.0001371315,0.0004642415],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8952217,"threshold_uncertainty_score":0.3505183,"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."}}