Bibliographic record
Abstract
Today it seems like we all have less free time than ever before. As a result, people are getting away for only a few days, doing it more often, and looking for escapes that are unique and off the beaten path. In keeping with that trend, Hideaways is an invaluable resource for all kinds of golf travel. The book's thirty-five hideaways-twenty-five top picks plus ten honorable mentions-showcase the most beautiful lodgings at top-rated golf courses of the United States, Canada, Mexico, Puerto Rico, Jamaica, and the Dominican Republic.In order to reflect the many different types of vacationing golfers--from the hardcore golfer who will spend an entire day on the links to the more casual player whose day will include other activities-a variety of resort styles are represented, from old school to new and trendy, and from those with great courses and an ordinary spa to those with ordinary courses and a spectacular spa. With its inclusion of course ratings, photographs, diagrams, and detailed hole-by-hole accounts, the book is primarily for the golfing enthusiast, yet the wide scope of information about the spas, rooms, lodging experience, and surrounding environs will appeal to the non-golfer as well, assuring the traveler of an ideal trip. With its useful contents and attractive packaging, Golf Hideaways is sure to be the definitive guidebook on its subject.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.087 | 0.024 |
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".