{"id":"W7017353684","doi":"","title":"ARCHIVEIT-3490-PATCH-OGTSLC-20130129184552-00000-crawling114.us.archive.org-6682.warc.gz","year":2013,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Identification (biology); Product (mathematics)","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":[0.001048725,0.002903428,0.001792382,0.006949048,0.002213121,0.006609125,0.003253665,0.002309436,0.8417853],"category_scores_gemma":[0.008744673,0.002331749,0.001257782,0.009340853,0.000792148,0.005371231,0.005347594,0.001619691,0.9352103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002627175,"about_ca_system_score_gemma":0.002095265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04988813,"about_ca_topic_score_gemma":0.04925868,"domain_scores_codex":[0.9991009,0.00007309856,0.00005335946,0.0002051034,0.0004009906,0.0001665474],"domain_scores_gemma":[0.9942947,0.0007424106,0.0001863046,0.002006427,0.002010351,0.0007597287],"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":[0.00002541594,0.00001065604,0.0001139591,0.00009864291,0.000003981,0.000007814411,0.00004579121,0.0000275393,0.00009038965,0.0003130423,0.9886519,0.01061076],"study_design_scores_gemma":[0.00002943414,0.000006026792,0.0007179978,0.00007684102,0.000004322334,0.00002645921,0.00004316446,0.0001064596,0.0004984422,0.0003909992,0.9980794,0.00002037098],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0004226883,0.0002363642,0.003365754,0.000422876,0.0002495098,0.0001785204,0.5116735,0.2024849,0.2809659],"genre_scores_gemma":[0.002772616,0.0004629935,0.004816647,0.0004956913,0.0001270908,0.0002509643,0.4826787,0.1920511,0.3163443],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1582147,"threshold_uncertainty_score":0.225674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006515149422086797,"score_gpt":0.1902025511455974,"score_spread":0.1836874017235106,"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."}}