Visitor Use and Activities Detected Using Trail Cameras at Forest Restoration Sites
Bibliographic record
Abstract
ABSTRACT We used trail cameras to monitor human visits and activities at two sites in northeast Indiana being restored to bottomland hardwood forests. These sites, managed as nature preserves, are close to cities, where trails and parking lots have been added for ease of access. In this study, trail cameras were successfully used to capture visitation rates and activity types. The two sites had median visitor use rates of 1 and 13 visitors per day. Across both sites, “parking lot use only” (62%), hikers (30.2%), and bicyclists (5%) accounted for more than 97% of site visits. Overall, most weekday visitor-time occurred during daylight hours, peaking at lunch and evening. Mean total number of daily visitors was higher during weekends; however, total daily visitor-time did not vary between days of the week. Michaelis-Menten rarefaction models of sampling efficiency across the study’s four camera stations suggest sampling duration of 27 to 55 days to accurately estimate mean daily visitor counts and 3 to 40 days to detect half the maximal numbers of observed activities. Study estimates of visitation provide land managers with information for accommodating visitor use activities on the restored sites and offer inputs for cultural ecosystem services assessments and associated economic analyses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".