Reply by Authors
Bibliographic record
Abstract
No AccessJournal of UrologyAdult Urology1 Aug 2021Reply by Authorsis a reply to letterEditorial Comment Avril Lusty, R. Christopher Doiron, Christopher M. Booth, Marlo Whitehead, and D. Robert Siemens Avril LustyAvril Lusty Department of Urology, Queen’s University, Kingston, Ontario, Canada More articles by this author , R. Christopher DoironR. Christopher Doiron Department of Urology, Queen’s University, Kingston, Ontario, Canada More articles by this author , Christopher M. BoothChristopher M. Booth Department of Oncology, Queen’s University, Kingston, Ontario, Canada Division of Cancer Care and Epidemiology, Queen’s University Cancer Research Institute, Queen’s University, Kingston, Ontario, Canada More articles by this author , Marlo WhiteheadMarlo Whitehead ICES-Queen’s, Health Services and Policy Research Institute, Queen’s University, Kingston, Ontario, Canada More articles by this author , and D. Robert SiemensD. Robert Siemens *Correspondence: Victory4, Kingston General Hospital, 76 Stuart St., Kingston, Ontario , K7L 2V7 telephone: 613-548-2411; E-mail Address: [email protected] Department of Urology, Queen’s University, Kingston, Ontario, Canada Department of Oncology, Queen’s University, Kingston, Ontario, Canada Division of Cancer Care and Epidemiology, Queen’s University Cancer Research Institute, Queen’s University, Kingston, Ontario, Canada More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000001715.02AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Reply by Authors." The Journal of Urology, 206(2), pp. 268–269 © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsRelated articlesJournal of UrologyMay 13, 2021, 12:00:00 AMEditorial Comment Volume 206Issue 2August 2021Page: 268-269 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Avril Lusty Department of Urology, Queen’s University, Kingston, Ontario, Canada More articles by this author R. Christopher Doiron Department of Urology, Queen’s University, Kingston, Ontario, Canada More articles by this author Christopher M. Booth Department of Oncology, Queen’s University, Kingston, Ontario, Canada Division of Cancer Care and Epidemiology, Queen’s University Cancer Research Institute, Queen’s University, Kingston, Ontario, Canada More articles by this author Marlo Whitehead ICES-Queen’s, Health Services and Policy Research Institute, Queen’s University, Kingston, Ontario, Canada More articles by this author D. Robert Siemens Department of Urology, Queen’s University, Kingston, Ontario, Canada Department of Oncology, Queen’s University, Kingston, Ontario, Canada Division of Cancer Care and Epidemiology, Queen’s University Cancer Research Institute, Queen’s University, Kingston, Ontario, Canada *Correspondence: Victory4, Kingston General Hospital, 76 Stuart St., Kingston, Ontario , K7L 2V7 telephone: 613-548-2411; E-mail Address: [email protected] More articles by this author Expand All Advertisement PDF DownloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.022 | 0.024 |
| Insufficient payload (model declined to judge) | 0.116 | 0.090 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".