Bibliographic record
Abstract
Introduction. Part I: Introducing Vancouver and Victoria. Chapter 1: Discovering the Best of Vancouver and Victoria. Chapter 2: Digging Deeper into Vancouver and Victoria. Chapter 3: Deciding When to Go. Part II: Planning Your Trip to Vancouver and Victoria. Chapter 4: Managing Your Money. Chapter 5: Getting to Vancouver and Victoria. Chapter 6: Catering to Special Travel Needs or Interests. Chapter 7: Taking Care of the Remaining Details. Part III: Vancouver. Chapter 8: Arriving and Getting Oriented. Chapter 9: Checking In at Vancouver's Best Hotels. Chapter 10: Dining and Snacking in Vancouver. Chapter 11: Exploring Vancouver. Chapter 12: Shopping the Local Stores. Chapter 13: Following an Itinerary: Four Great Options. Chapter 14: Going Beyond Vancouver: Three Great Day Trips. Chapter 15: Living It Up After Dark: Vancouver's Nightlife. Part IV: Victoria. Chapter 16: Arriving and Getting Oriented. Chapter 17: Checking In at Victoria's Best Hotels. Chapter 18: Dining and Snacking in Victoria. Chapter 19: Discovering Victoria's Best Attractions.8 Chapter 20: Shopping the Local Stores. Chapter 21: Following an Itinerary: Two Great Options. Chapter 22: Living It Up After the Sun Goes Down: Victoria Nightlife. Part V: The Part of Tens. Chapter 23: Ten Things You Can't Live Without in Vancouver. Chapter 24: Ten Things You Can't Live Without in Victoria. Chapter 25: Ten Celebrities You Didn't Realize Were from the Area. Appendix: Quick Concierge. Index.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".