{"id":"W6905649480","doi":"10.15468/dl.rmfsfq","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); Download; Alien; Range (aeronautics); State (computer science)","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":[0.0008816264,0.00197326,0.001446935,0.0047007,0.0009398636,0.002392095,0.002556452,0.001864699,0.165708],"category_scores_gemma":[0.005727948,0.0008782809,0.001174507,0.009354051,0.0004283536,0.002163635,0.002463573,0.001741901,0.2223366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001407643,"about_ca_system_score_gemma":0.002190825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01996434,"about_ca_topic_score_gemma":0.03246243,"domain_scores_codex":[0.9990193,0.0001291129,0.0001236474,0.0003567467,0.0002036683,0.0001676574],"domain_scores_gemma":[0.9976999,0.0006434858,0.000219799,0.0005899858,0.0005809829,0.0002658037],"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":[0.00003068655,0.00001127707,0.0004077019,0.0004925388,0.00001295228,0.00001371649,0.00002002613,0.0001251483,0.0001166794,0.0003314786,0.9969694,0.001468374],"study_design_scores_gemma":[0.00007746245,0.0000107339,0.002001539,0.0001864796,0.00001441271,0.000036364,0.00006947399,0.0001605428,0.0001966468,0.0007761393,0.9964521,0.00001809431],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000464071,0.00002441661,0.00003912378,0.00003284909,0.00001264818,0.00000500632,0.9988543,0.0003543678,0.0006308141],"genre_scores_gemma":[0.000164428,0.00003183709,0.0001863702,0.00004601335,0.000003810232,0.00004110865,0.9988877,0.0001415442,0.0004972108],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8342921,"threshold_uncertainty_score":0.5543483,"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."}}