{"id":"W6924830742","doi":"10.15468/dl.zg6su9","title":"Occurrence Download","year":2024,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biodiversity; Download; Barcode; Invertebrate; Herring; Herbarium","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":["insufficient_payload"],"category_scores_codex":[0.000887115,0.002326546,0.002025815,0.005866948,0.001308479,0.00398188,0.002890059,0.002337806,0.3507333],"category_scores_gemma":[0.006596605,0.0009843811,0.001577796,0.009342402,0.0003975756,0.004081953,0.003782406,0.002251153,0.4279324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00154259,"about_ca_system_score_gemma":0.002107844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01593281,"about_ca_topic_score_gemma":0.03114488,"domain_scores_codex":[0.9987912,0.0001434292,0.0001602658,0.0004545203,0.0002638291,0.0001868413],"domain_scores_gemma":[0.9973447,0.0006855861,0.000215133,0.0005994822,0.0008219097,0.000333345],"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":[0.00004349664,0.00001306765,0.0003454593,0.000597731,0.00001200402,0.00002003069,0.00002364183,0.00007994658,0.00008632761,0.0003882359,0.9952143,0.003175758],"study_design_scores_gemma":[0.0000633653,0.00001243321,0.001283975,0.0001977858,0.00001229057,0.00004462265,0.00008677469,0.0002081248,0.0001404878,0.001183737,0.9967479,0.00001852748],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006171431,0.00005887232,0.0001264852,0.00007155212,0.00002727149,0.00001217821,0.9962606,0.001385654,0.001995678],"genre_scores_gemma":[0.0002645479,0.00006755741,0.0004967643,0.0001024477,0.00001103234,0.00006077034,0.9969061,0.0004978081,0.00159292],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6492667,"threshold_uncertainty_score":0.9261002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01708767307206114,"score_gpt":0.2335971948231368,"score_spread":0.2165095217510757,"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."}}