Climate Change, Ecosystem Loss and Flood Risk: Taking Stock using Burlington Case
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
Extreme weather events, climate change, and biodiversity loss are connected by both cause and solution. The impacts of climate change are already apparent as the frequency and magnitude of extreme weather events are increasing, undermining progress made across the globe toward sustainable development. These impacts are magnified by unsustainable and unplanned development, leading to lost biodiversity and ecosystem services, further reducing the ability of communities to respond and recover. As warming increases, the frequency and intensity of these hazards will also increase while at the same time making it more difficult to adapt to and mitigate disasters—the aftermath of hazards. Nature-based solutions provide opportunities to mitigate and adapt to climate change impacts, reduce the risk of disasters, enhance biodiversity, and build sustainable and resilient communities. They are cost-effective approaches that conserve, restore and enhance the natural environment. Using the 2014 flood event in the City of Burlington (Ontario, Canada), this study takes stock of flood risk in the region and how nature-based solutions provide significant co-benefits toward reducing disaster risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it