Visitor Use and Activities Detected Using Trail Cameras at Forest Restoration Sites
Bibliographic record
Abstract
<h3>ABSTRACT</h3> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".