The Effect of Seasonal Climatic Anomalies on Zoo Visitation in Toronto (Canada) and the Implications for Projected Climate Change
Why this work is in the frame
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Bibliographic record
Abstract
This study uses a multi-year temporal climate analogue approach to explore zoo visitor responses to seasonal climatic anomalies and assess the impacts of projected climate change on zoo visitation in Toronto, Canada. A new method for selecting a representative weather station was introduced which ranks surrounding stations based on “climatic distance” rather than physical distance alone. Two years representing anomalously warm temperature conditions and two years representing climatically normal temperature conditions were identified for each season from within the study period from 1999 to 2015. Two years representing anomalously wet precipitation conditions and two years representing anomalously dry precipitation conditions were also identified. F-tests and t-tests were employed to determine if the apparent differences in zoo visitation between the temperature and precipitation paired groupings were statistically significant. A “selective ensemble” of seasonal Global Climate Model (GCM) output from the Intergovernmental Panel on Climate Change Fifth Assessment Report was used to determine when these anomalous temperature and precipitation conditions may become the norm in the future. When anomalously warm winters and springs occurred within the historical record, total zoo visitation in those seasons increased significantly. Inversely, when anomalously warm summers occurred, total summer season zoo visitation decreased significantly. Temperature anomalies in the autumn season did not result in any significant differences in total autumn season zoo visitation. Finally, apart from in the spring season, there were no significant differences in total zoo visitation between anomalously wet and dry seasons.
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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 | 0.000 |
| 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 it