Make your mark, Eye Dare You | Faites votre empreinte, je vous mets au défi
Bibliographic record
Abstract
E ye dare you!The challenge to promote eye health for October Eye Health Month (EHM) is back.In preparation of October EHM, CAO is once again hosting the Eye Dare You challenge to encourage members to extend the eye health brand and to promote proactive eye health at a local level.Radio announcements, PowerPoint presentations, print ready ads, a media outreach kit, and other resources can all be downloaded from the CAO member portal at opto.ca.Members can also read about last year's EHM projects for ideas and innovative ways to promote awareness in their community.Echo optometry's brand of proactive eye health in your area and send brief details of your EHM project to eyedareyou@opto.ca.Your name will be entered in a draw to win a Nintendo Wii Game console.In addition, the province with the most members (per capita) participating in the challenge will be featured in the November issue of the CJO.Promoting awareness is perhaps more important this year than in previous years because Fall 2008 marks the launch of the new television campaign, "An optometrist knows your eyes inside and out".
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.005 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".