Powering the Future: How Hamilton Health Sciences Put Cogeneration to Work for Healthcare
Bibliographic record
Abstract
Ontario's Energy ChallengeEnergy conservation and generation initiatives are becoming increasingly important in Ontario.More and more, organizations are being asked or even mandated to actively pursue energy-conservation strategies.Hospitals are no exception.Early last year, the Government of Ontario passed Bill 21, The Energy Conservation Responsibility Act, which will see the Ontario Power Authority assume responsibility for ensuring hospitals, universities and other municipal buildings participate in programs to increase conservation and demand management.Under this legislation, hospitals are required to prepare energyconservation strategies on a regular basis and report on energy consumption, proposed conservation measures and progress on achieving results.While social responsibility is an important component of energy conservation for hospitals, there is also the very real and immediate issue of ensuring that hospitals have access to a stable and uninterrupted power supply.In August 2003, this issue was highlighted when a series of power surges over a 12-second period triggered a cascade of shutdowns at more than 100 generating plants in eight US states and across Ontario.The result was the biggest blackout in North American history.Over 50 million people were affected, and 61,800 megawatts of power were lost (Independent Electricity System Operator 2003).The blackout tested the limits of emergency backup power within the province's hospitals and drove home the need for alternative energy sources.Less than a year later, the Honourable Dwight Duncan (2004), Ontario's energy minister, stated that if the province is going to keep pace with the demand for electricity, it "will need to refurbish, rebuild, replace or conserve 25,000 megawatts
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".