David MacLeod is a Senior Environmental Specialist at the Toronto Environmental Office in the City of Toronto.
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
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.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.054 | 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