{"id":"W6924885320","doi":"10.15468/dl.v7cgn3","title":"Occurrence Download","year":2022,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Download; Range (aeronautics); State (computer science); Feature (linguistics)","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":[0.0009643363,0.002473996,0.001707378,0.005217333,0.001055966,0.0025586,0.003031744,0.00218829,0.1268993],"category_scores_gemma":[0.00522318,0.000949935,0.001215269,0.009749561,0.0005004768,0.002389579,0.002618771,0.002051048,0.195108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001758025,"about_ca_system_score_gemma":0.002469438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0199136,"about_ca_topic_score_gemma":0.03443072,"domain_scores_codex":[0.9989282,0.0001440455,0.0001352757,0.0003917826,0.0002338165,0.0001669032],"domain_scores_gemma":[0.9979226,0.0005547718,0.0002101858,0.0005207749,0.0005194352,0.0002723236],"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":[0.00004486706,0.00001550793,0.0004192406,0.0006696527,0.00001711654,0.00001853721,0.00002274262,0.0001483899,0.0001585113,0.0004185004,0.9965165,0.001550557],"study_design_scores_gemma":[0.00009318675,0.00001087386,0.001715341,0.0001895954,0.00001487517,0.0000446348,0.00006372493,0.0001823367,0.0002390304,0.0008562754,0.9965706,0.00001953565],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005142884,0.00003563496,0.00004407622,0.00003297164,0.0000117674,0.000005705021,0.998732,0.0004208172,0.0006655873],"genre_scores_gemma":[0.0001536029,0.0000380992,0.0002000879,0.0000445279,0.000003107157,0.00003686912,0.9989882,0.00013531,0.0004000959],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8731006,"threshold_uncertainty_score":0.4245206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01312551129456277,"score_gpt":0.2383362685590715,"score_spread":0.2252107572645087,"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."}}