Modeling the sensitivity of outdoor recreation activities to climate change
Bibliographic record
Abstract
This study develops a methodological framework to analyze the climate sensitivity as well as climate change impacts on outdoor recreation activities, applied to a case study of 2 lidos in the city of Zurich. A negative binomial regression is used to link daily data on lido entries with weather variables for the period 2003 to 2010, while controlling for non-climatic factors (such as weekends and school holidays). It shows that the number of lido visitors is significantly determined by weather conditions such as temperature and rainfall. Results from the regressions are combined with output of a stochastic weather generator to fully explore climate risks on a monthly and annual basis under current conditions. This setup is furthermore used to predict near future climate change impacts on lido entries using 3 emission scenarios. It shows that near future climate change characterized by temperature increases and reduced precipitation levels in late summer will have a positive effect on the number of lido visitors. While the expected increase of the annual number of visitors ranges from 9 to 19%, increases of >30% are expected for August and September. © Inter-Research 2012.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".