A mixed-modes approach for estimating hiking on trails through diverse forest landscapes: the case of the Appalachian Trail
Bibliographic record
Abstract
Many hiking trails traverse the forests and public lands across North America. It has therefore become important for federal management to gain an understanding of total use on these trails. However, there has never been a formal attempt to estimate hiking on these long, backcountry trails. This paper presents an approach that utilizes two survey instruments (exit-site tallies and a trail-user questionnaire) and two primary estimation components (standard and augmented sites) to estimate hikers over a spatial and temporal span. For illustrative purposes, the methodology is applied to a 175 km segment of the Appalachian Trail from 1 June through 14 August 2007. Two alternative estimation methodologies are presented and compared. The model-based approach may be preferred to the design-based approach when sample size is small because it smoothes erratic strata estimates and yields smaller standard errors. However, the design-based approach relaxes an assumption and is more appropriate as sample size increases. In our survey of the Appalachian Trail, there was a 5.6% difference between the visitation estimates based on these two approaches, and such stability reinforces confidence in the methodology.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".