Bibliographic record
Abstract
DSM planners need to consider business capital investment cycles within program designs to optimize program participation, manage free-ridership, and adjust program delivery strategies. Macroeconomic conditions form a broad set of parameters that the DSM planner must monitor and implement adaptation strategies accordingly. The economic uncertainty following the recession of 2008-2009 has brought the potential impacts of the economy on DSM participation to the forefront for DSM planners in all jurisdictions. The impact of economic uncertainty is further exacerbated in Ontario by the broader uncertainties related to re-introducing major industrial DSM programs after many years with no activity. This research project will focus on answering the following key questions: 1. What are the key macroeconomic factors impacting industrial DSM program performance and energy efficiency levels? What is the quantification of the correlation between these factors? At a minimum, directionally what is the correlation and strength of the correlation? In particular how closely does capital investment correlate with energy efficiency and DSM program participation levels? 2. Is there a disproportionate growth or decline during various economic conditions between industrial sub-sectors? Scope of Research: 1. Program participation impacts for at least five industrial programs in North America. 2. Large manufacturing / industrial sectors in Ontario in terms of energy use, changes in capital investment, etc. 3. The research should examine business investment and capacity utilization trends from a period long enough to capture a few business cycles.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".