Bibliographic record
Abstract
Optometry & Vision Science (OVS) plans a Feature Issue to provide a showcase for latest discoveries related to ocular surface research to inform clinicians, researchers, industrial partners, patients, policy makers and other stakeholders about current research and advances, and to promote and inspire additional thinking on this topic. OVS invites original articles and review papers that highlight research and clinical advances, including, but not limited to: Anatomy and physiology of the ocular surface Epidemiology of ocular surface disease Links between the ocular surface and surgical outcomes The ocular surface in contact lens wear Ocular surface diagnostics and novel outcome measures Management and treatment of ocular surface disease Ocular surface features and dry eye disease Impact of systemic disease and its treatment on the ocular surface Impact of genetic and environmental factors on the ocular surface Vision and dry eye disease Social impact of dry eye disease Patient-reported outcomes in dry eye disease As in previous Feature Issues, OVS will publish study results from original research and clinical perspectives together in a single publication. Submissions for this Feature Issue will be coordinated by a distinguished Guest Editorial Team and the Editor-in-Chief. Guest Editorial Team Jennifer Craig - Feature Issue Lead Editor, Professor, University of Auckland, New Zealand. Eric Papas - Emeritus Professor, UNSW Sydney, Australia Fiona Stapleton - Professor, UNSW Sydney, Australia James S. Wolffsohn – Professor, Aston University, Birmingham, United Kingdom Laura Downie - Associate Professor, University of Melbourne, Australia Lyndon Jones – Professor, University of Waterloo, Canada Nicole Carnt - Associate Professor, UNSW Sydney, Australia We anticipate that there will be great interest in this publication from clinicians and both basic and clinical scientists. All papers accepted in the peer review process will be published in OVS in a timely fashion. The window for manuscript submission is November 8, 2023 to March 8, 2024, with anticipated publication in the autumn months of 2024. Early submissions are encouraged and will receive priority for publication on the journal website at: https://journals.lww.com/optvissci/pages/default.aspx Please prepare submissions according to the OVS Instructions for Authors: https://edmgr.ovid.com/ovs/accounts/ifauth.htm and submit them online at https://www.editorialmanager.com/ovs/default2.aspx noting that your paper is being submitted for consideration of this Feature Issue. Contact the Editorial Office ([email protected]) or the Editor in Chief ([email protected]) with questions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".