Bibliographic record
Abstract
In today's world, global warming (GW) and the resulting climate change are a significant threat to humans, plants and animals. The main contributor to GW is greenhouse gases (GHGs) that are created from the burning of fossil fuels, mainly for electrical power. Hence, the way forward in safeguarding the future of life on planet earth is to reduce on our GHG emissions and move rapidly to the utilization of renewable energy resources that are abundantly available. There are numerous advantages in implementing renewable energy projects versus the use of fossil fuels in meeting individual or community energy demands. With renewable energy, an individual or community will be able to have a more diverse and stable long-term energy supply, considering fossil fuel resources are slowly being depleted. Small-scale renewable energy projects, especially in remote communities which are currently served by diesel-generated electricity, offset the community's use of diesel fuel. Although there will be times when renewable energy is not available and a back-up source of power is required, the long-term cost of energy may be reduced. And the use of indigenous energy can contribute to a nation (or region's) energy security by significantly reducing its dependence on imported oil (assuming it is not an oil exporter). There are numerous renewable energy resources available globally (wind, solar, biomass, falling water, geothermal) that can be used for individual or community energy projects. Community energy projects are distinguished from other renewable energy projects in which members of the community are subscribers who use the electricity produced by the project, even though each individual does not solely own the equipment. This chapter presents an overview of how energy captured from the sun can be utilized at the community level by installing solar photovoltaic systems in the form of a solar garden or solar farm or solar power plant to generate electrical energy in meeting some, if not all, of the community total energy demand. In addition, readers will be exposed to three common ownership models and their benefits, barriers affecting the adoption of such projects and selected examples of such projects that have been completed or are in the conceptualization or construction phase within North America, Europe, South America and East Africa.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.274 | 0.090 |
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".