Bibliographic record
Abstract
A captivating narrative guidebook to inspire everyone, no matter their abilities, to get outside and experience the country’s natural landscapes. When Kellisa Kain was born premature with significant developmental and physical disabilities, she wasn’t expected to survive her first 24 hours. She defied the odds, and 20 years later she and her father, Christopher Kain, have pushiked using a specialized mobility chair across the entire United States. In Rugged Access for All: A Guide for Pushiking America’s Diverse Trails with Mobility Chairs and Strollers, Chris and Kellisa showcase some of the greatest trails across the US that can be completed while pushiking—hiking with someone in a wheelchair, mobility chair, or stroller. Part narrative, part guide, this book includes detailed trail descriptions, trail maps, tips for hiking with a stroller or mobility chair, and vibrant stories from Chris and Kellisa’s own experiences hiking in all 50 states. The featured trails vary in difficulty, from deserts to mountains and everything in between. Sometimes even a stroll around the block can have frustrating barriers to those with wheels, and this can lead to families staying inside too often. Rugged Access for All gives families the knowledge, confidence, and direction to travel and experience the wonders of nature, no matter what mobility challenges they may face.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.865 | 0.832 |
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".