Bibliographic record
Abstract
am writing this editorial during the second week of November while Southern Ontario enjoys a few days of Indian Summer.Only a week ago, Western Canada was freezing under its second bout of winter storms and the USA was trying to recover from the madness of their elections.Meanwhile we in Canada mourn the passing of Alex Trebek and Howie Meeker.Any way you look at it, this year is one of trauma and loss for many people, and while we would like to forget it, I don't think that is likely.The pandemic has certainly had its effect on our profession.The practice of optometry in post-pandemic times will be very different from what it was before 2020.The old truisms of practice, both professional and financial, will have to be rewritten.However, with change comes opportunity and I am already seeing individuals and groups of practitioners working to adapt to the new conditions.I expect that some of our colleagues who were considering retirement before the pandemic hit, will decide to make the jump rather than commit the considerable resources required to practise safely under the new conditions.We wish them well.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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; both teacher heads agree on what is shown here.
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".