Regional Renewable Energy Cooperatives
Bibliographic record
Abstract
We are building a multidisciplinary research program linking researchers in agriculture, business, earth science, engineering, humanities and social science. Our goal is to match renewable energy supply and reformed energy demands. The program will be focused on (i) understanding and modifying energy demand, (ii) design and implementation of diverse renewable energy networks. Geomatics technology will be used to map existing energy and waste flows on a neighbourhood, municipal, and regional level. Optimal sites and combinations of sites for solar and wind electrical generation (ridges, rooftops, valley walls) will be identified. Geomatics based site and grid analyses will identify best locations for energy production based on efficient production and connectivity to regional grids and transportation. Design of networks for utilization of waste streams of heat, water, animal and human waste for energy production will be investigated. Agriculture, cities and industry produce many waste streams that are not well utilized. Therefore, establishing a renewable energy resource mapping and planning program for electrical generation, waste heat and energy recovery, biomass collection, and biochar, biodiesel and syngas production is critical to regional energy optimization. Electrical storage and demand management are two priorities that will be investigated. Regional scale cooperatives may use electric vehicle batteries and innovations such as pump storage and concentrated solar molten salt heat storage for steam turbine electrical generation. Energy demand management is poorly explored in Canada and elsewhere – our homes and businesses operate on an unrestricted demand. Simple monitoring and energy demand-ranking software can easily reduce peaks demands and move lower ranked uses to non-peak periods, thereby reducing the grid size needed to meet peak demands. Peak demand strains the current energy grid capacity and often requires demand balancing projects and infrastructure that is highly inefficient due to overall low utilization.
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".