Bibliographic record
Abstract
As Marketing4ECPs' Senior Content Strategist, Kaia Pankhurst creates and implements content strategies for eye care practices all over North America.Outside of the office, Kaia is a musician, activist, and professional wrestler.You can reach Kaia at marketing4ecps.comW e live in a digital world, so a website is essential if you want your practice to get noticed.But it's not necessarily enough to have just a website; if you want to compete with the other practices in town (especially the big box stores), you need a great website.Ideally, you'll hire an agency specializing in building high-converting websites, and that understands the eye care industry.Whether you're building a new website or updating your current one, these five steps could be enough to elevate your website to the next level. CONSIDER YOUR USERThe most important thing to keep in mind when building or updating your practice website is user experience.User experience or UX should apply to almost every part of your website.For example, 53% of users will abandon a page if it doesn't load within three seconds.Users also don't read website content word for word; they skim it.So your website content should be broken into small chunks with headlines for easy scanning.Keeping your user's information needs at the forefront is an excellent start.
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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.068 | 0.037 |
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".