Bibliographic record
Abstract
The Shediac Smart Energy Community Project and its component the Shediac Residential Energy Study were conceived by our friends at New Brunswick Power (NB Power) with inspiration from the Town of Shediac and in response to the needs of the time: to reduce fossil fuel use for electricity generation, to increase grid capacity, and to maintain grid stability. The National Research Council of Canada (NRC) was privileged to have been chosen to serve as the Research Partner to help make it happen together with NB Power and Siemens. This book is a compilation of the ten research reports that emerged from the Shediac Residential Energy Study at its conclusion in the spring of 2025. It is a complete summary of what was done and what was learned from the primary analyses of the data from all sources. Chapters 1-3 provide the research design details. Chapters 4-9 provide detailed results for each component of the study: household energy use patterns; rooftop solar photovoltaic generation with battery energy storage; time-of-day electricity rates; cold-climate mini-split heat pumps; smart thermostats; and motivations for energy behaviours. Chapter 10 summarizes the whole and highlights implications.to reduce fossil fuel use, increase grid capacity, and maintain grid stability.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".