{epub download} Lonely Planet Canada by Brendan Sainsbury, Jennifer Bain, Joel Balsam, Jonny Bierman, Bianca Bujan
Bibliographic record
Abstract
Lonely Planet Canada by Brendan Sainsbury, Jennifer Bain, Joel Balsam, Jonny Bierman, Bianca Bujan Download Book ➡ Link Read Book Online ➡ Link Lonely Planet Canada Brendan Sainsbury, Jennifer Bain, Joel Balsam, Jonny Bierman, Bianca Bujan Page: 704 Format: pdf, ePub, mobi, fb2 ISBN: 9781838697068 Publisher: Lonely Planet Free ebook download forums Lonely Planet Canada Overview Lonely Planet's local travel experts reveal all you need to know to plan the trip of a lifetime to Canada. Discover popular and off the beaten track experiences from visiting Banff - the world's third-oldest national park - to making a poutine pilgrimage to Quebec City to try Canada's most famous dish, and spotting black bears and grizzlies in the Bella Coola Valley. Build a trip to remember with Lonely Planet's Canada travel guide: Our classic guidebook format provides you with the most comprehensive level of information for planning multi-week trips Updated with an all new structure and design so you can navigate Canada and connect experiences together with ease Create your perfect trip with exciting itineraries for extended journeys combined with suggested day trips, walking tours, and activities to match your passions Get fresh takes on must-visit sights including Stanley Park - one of North America's largest urban green spaces, plus Granville Island Public Market Special features on indigenous Canada, hiking and camping guide, and choosing your train journey Expert local recommendations on when to go, eating, drinking, nightlife, shopping, accommodation, adventure activities, festivals, and more Essential information toolkit containing tips on arriving; transport; making the most of your time and money; LGBTIQ+ travel advice; accessibility; and responsible travel Connect with Canadian culture through stories that delve deep into local life, history, and traditions Inspiring full-colour travel photography and maps including a pull out map of Canada Covers Ontario, Quebec, Nova Scotia, New Brunswick, Prince Edward Island, Newfoundland & Labrador, Manitoba, Saskatchewan, Alberta, British Columbia, Yukon Territory, Northwest Territories, Nunavut Create a trip that's uniquely yours and get to the heart of this extraordinary country with Lonely Planet's Canada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.761 | 0.643 |
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".