A multiple timescales approach to assess urgency in adaptation to climate change with an application to the tourism industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As climate change adaptation is increasingly discussed and becoming a mainstream concept, different types of users are asking themselves if and when they should develop an adaptation strategy, often not knowing where to begin. Climate experts, on the other hand, have access to an enormous amount of data that could be useful to users but often do not know how to translate it into something practical. Both users and experts can be linked through two timescales, the system lifespan and climate vulnerability. While the system lifespan relies on the user’s estimation of his planning timeframe, the climate vulnerability is estimated from climate model projections and observations. We propose a simple tool to relate user and climate expert knowledge by combining the two timescales. To be reliable, the interconnection implies a dialogue to first identify what sensitive climate variable will impact the system and subsequently the extent of the impact. Climate data can then be used to identify, with the use of a simple graph, how sensitive a system is likely to be and help users position themselves about the urgency of adaptation. The concept has been successfully presented and applied to the tourism industry, notably the ski industry, which is showcased in this paper.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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