David MacLeod is a Senior Environmental Specialist at the Toronto Environmental Office in the City of Toronto.
Bibliographic record
Abstract
There is a growing movement in response to climate change, known as climate change adaptation. In the general media, most attention has been focused on the need for climate change mitigation, which is action to reduce greenhouse gases that have caused climate change. Climate change adaptation is action to reduce the negative impacts of climate change. Municipalities are now responding to the need to adapt to the long lasting change in weather patterns generated by climate change. For example, in July 2008, Toronto council unanimously adopted a climate change adaptation strategy for Toronto titled, “Ahead of the Storm.”1 Figure 1, has been developed in the Toronto Environment Office to help ex-plain the concepts of climate change mitigation and adaptation. Given the alarming rates of climate change occurring, successful climate change mitigation is absolutely essen-tial. Climate change adaptation is, un-fortunately, going to be necessary, be-cause it may take many decades for the world to reach greenhouse gas re-duction targets. In Toronto, key antic-ipated local impacts are expected to be increased probability of extreme weather such as heat, drought, rain, snow and ice storms, and winds. Figure 2 was developed to provide examples of climate change mitigation and adaptation actions. Both types of actions are necessary, and some actions such as planting trees, buying local food and installing green roofs can help with both.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.195 | 0.027 |
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".