Bibliographic record
Abstract
As Alberta increases conventional solar power generation, land-use conflicts with agriculture increase. A solution that enables low-carbon electricity generation and continued (in some cases, increased) agricultural output is the co-locating of solar photovoltaics (PV) and agriculture: agrivoltaics. This review analyzes policies that impact the growth of agrivoltaics in Alberta. Solar PV-based electricity generation is governed by three regulations based on system capacity. In addition, agrivoltaics falls under various legislations, frameworks, and guidelines for land utilization. These include the Land Use Framework, Alberta Land Stewardship Act, Municipal Government Act, Special Areas Disposition, Bill 22, and other policies, which are reviewed in the agrivoltaics context. Several policies are recommended to support the rapid deployment of agrivoltaics. Openly accessible agrivoltaics research will help optimize agrivoltaic systems for the region, and can be coupled with public education to galvanize social acceptability of large-scale PV deployment. Clearly defining and categorizing agrivoltaics technology, developing agrivoltaics standards, making agrivoltaics technology-friendly regulations and frameworks, and developing programs and policies to incentivize agrivoltaics deployment over conventional PV will all accelerate the technology’s deployment. Through these measures, Alberta can achieve conservation and sustainability in the food and energy sectors while simultaneously addressing their renewable energy and climate-related goals.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".