A Pacific Crest Trail Quantitative Investigation of Land Management Issues
Bibliographic record
Abstract
The Pacific Crest Trail (PCT) is a National Scenic Trail that extends approximately 2,653 miles from the border of California and Mexico to the border of Washington State and Canada. This study examined land management and hiker issues that day, overnight, and thru-hikers experience in California while navigating the PCT in order to inform land managers. This quantitative survey, completed through Qualtrics, was disseminated to thru-hikers who completed the trail between 2017 and 2021, and individuals who used the trail for shorter durations. Survey questions focused on technology, Trail Angels, rule enforcement by land management agencies, the permitting process by the Pacific Crest Trail Association (PCTA), hiker education, and hiker demographics. Descriptive statistics were calculated and analyzed. Results indicate that most hikers prefer articles on the PCTA website, Leave No Trace (LNT) website, and certifications to gain education about the trail. They also felt that personal contacts were the most effective way to gain pre-trip education. Almost all hikers use technology on the trail, such as navigation or informational apps, emergency calls, taking photos, and listening to videos or music. More thru-hikers rely on navigational apps than day/overnight users. Many hikers felt there should be more rangers in the backcountry but also felt that social enforcement of rules is effective. Regarding Trail Angels, thru-hikers felt they were important for transportation, food/water, and shelter. Managerial implications for public land managers included a need to develop ways to educate users. This can be done in-person, online, or through signage. Increasing the presence of rangers or volunteers on the trail was also recommended, as well as developing area or jurisdiction-specific apps for information and education about trail conditions or rules. Additional education of sustainable hiking behaviors may help mitigate potential negative impacts hikers may have on the environment. Land managers must continue to assess what is currently being done and re-evaluate on an ongoing basis in order to preserve and maintain sustainable use of public lands.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".