Bibliographic record
Abstract
How can the health sector support Canada's net-zero ambition? As the Honourable Steven Guilbeault has clarified, if Canada is to achieve net-zero emissions by 2050, we will need “all-hands-on-deck.” Fiona A. Miller, Professor & Director at the Centre for Sustainable Health Systems, University of Toronto in Canada, explores Canada's net-zero ambition, looking in particular at the role of the health sector in decarbonisation. However, achieving Canada's net-zero ambition of neutral greenhouse gas emissions by 2050 is a complex and challenging goal that requires a whole-of-society approach. The initiative calls for all companies operating in Canada to voluntarily commit to developing and implementing a plan to achieve Canada's net-zero ambition, supported by clear technical standards and public reporting requirements. So far, the 2030 emissions reduction plan aims at “clean air and good jobs, a healthy environment and a strong economy.” The problem lies in the significant gap in the Federal Government's plans: the health sector's direct contribution to the net-zero transition. Healthcare is a highly resource-intensive and polluting industry, estimated at 5.2% of global emissions and increasing. Moreover, Canada's health sector is estimated to be the second most carbon-intensive in the world after that of the U.S. There is clearly work to do, Professor Miller evaluates.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.022 | 0.019 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".