Using a Multiyear Temporal Climate-Analog Approach to Assess Climate Change Impacts on Park Visitation
Bibliographic record
Abstract
Abstract Because of the perceived weather sensitivity of park visitation in Ontario, Canada, several previous assessments have examined the impact of climate change. However, these assessments have predominantly been based on modeling approaches (regression analysis). The current study uses a multiyear temporal climate-analog approach to reassess the impact of climate change on visitation to Pinery Provincial Park in southwestern Ontario based on the observed effects of historical climatic anomalies on park visitation from 2000 to 2016. Consideration was also given to major events such as the North American terror attacks on 11 September 2001 and the confounding effect that events such as this may have had on the results. There were no statistically significant relationships (at the 95% confidence level) between seasonal climatic anomalies and park visitation in Ontario during the winter or spring seasons. There was a weak statistical relationship between anomalously warm summer seasons and park visitation, when compared to summer seasons with climatically normal temperatures; however, the presence of nonclimatic variables may have confounded these results, producing a false positive. Autumn-season park visitation was most sensitive to climatic anomalies, with the warmest temperatures causing visitation to increase by 37%, the wettest conditions causing visitation to decrease by 11%, and the driest conditions resulting in a 24% increase. These observed seasonal temperature anomalies represent temporal climate analogs for projected climate change across the span of the twenty-first century. Thus, the results of this study suggest that previous assessments may have overestimated the positive impacts of projected climate change on park visitation in this region.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".