{"id":"W6887154752","doi":"10.15468/dl.rfx6n6","title":"Occurrence Download","year":2022,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Identification (biology); Range (aeronautics); Download; Data set","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":[0.0009595398,0.002102664,0.00164723,0.00528083,0.001046724,0.002682647,0.002837901,0.002055988,0.1719542],"category_scores_gemma":[0.006082288,0.0009857796,0.001191438,0.01049195,0.000449978,0.002468207,0.00260184,0.00197492,0.2295131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001645701,"about_ca_system_score_gemma":0.002415526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02140367,"about_ca_topic_score_gemma":0.03615531,"domain_scores_codex":[0.9988709,0.0001521447,0.0001490869,0.0004039104,0.0002385234,0.0001854047],"domain_scores_gemma":[0.9975058,0.0007053052,0.0002405914,0.0006218776,0.0006413703,0.0002849814],"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":[0.0000325076,0.00001090217,0.0003512255,0.0005782285,0.00001473448,0.00001511101,0.00002151108,0.0001134949,0.0001203286,0.0003980942,0.9969903,0.001353529],"study_design_scores_gemma":[0.00007189828,0.000008551224,0.001654904,0.0001853122,0.00001425423,0.00003569935,0.00005980244,0.0001193446,0.0001774274,0.0007717206,0.9968829,0.00001823099],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003757874,0.00002777463,0.00003936893,0.00003160545,0.00001140606,0.000004691243,0.9988664,0.0003092224,0.0006719271],"genre_scores_gemma":[0.0001447767,0.00003535338,0.0001785939,0.00004632291,0.000003484099,0.00003922768,0.9988961,0.0001427953,0.0005132604],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8280458,"threshold_uncertainty_score":0.5752441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01850871640220096,"score_gpt":0.2277224731265546,"score_spread":0.2092137567243537,"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."}}