{"id":"W6943401472","doi":"10.15468/dl.ksyq9w","title":"Occurrence Download","year":2023,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Download; Matching (statistics); Range (aeronautics); Real world data","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":[0.0009908463,0.002038602,0.001642532,0.004719489,0.001004253,0.002423838,0.002882832,0.002214334,0.1410215],"category_scores_gemma":[0.00582529,0.0009523297,0.001257495,0.009301265,0.0004701391,0.00221074,0.002625954,0.002073963,0.2080396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001712648,"about_ca_system_score_gemma":0.002471651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0227616,"about_ca_topic_score_gemma":0.03763075,"domain_scores_codex":[0.9989877,0.0001294483,0.0001315553,0.0003549766,0.0002276693,0.0001684868],"domain_scores_gemma":[0.9977816,0.0006121999,0.0002129232,0.0006011203,0.0005406588,0.0002515631],"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":[0.00003297644,0.00001261696,0.0004365426,0.0006196725,0.00001774782,0.00001780548,0.0000299859,0.0001551944,0.0001569501,0.0004379042,0.9964893,0.001593442],"study_design_scores_gemma":[0.00007208045,0.000007910997,0.001756419,0.0001901381,0.00001492504,0.00003872539,0.00007449445,0.0001620226,0.0002122581,0.0007295216,0.9967235,0.00001799736],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004356056,0.00002633873,0.00004837937,0.00003295548,0.00001167596,0.000005328432,0.9988369,0.0004129163,0.0005818837],"genre_scores_gemma":[0.0001554995,0.00003087393,0.0002188819,0.00004000874,0.000002858253,0.00004044555,0.9989675,0.0001532911,0.0003906361],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8589785,"threshold_uncertainty_score":0.471764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02365312271414112,"score_gpt":0.2392044605254057,"score_spread":0.2155513378112646,"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."}}