Bibliographic record
Abstract
<div> Using a trellis to plant vegetables and fruits can double or triple the yield per acre as well as reduce diseases/pests, ease harvesting and make cleaner produce. Cultivars such as cucumbers, grapes, kiwi, melons, peas, passion fruit, pole beans, pumpkins, strawberries, squash, and tomatoes are all grown with trellises. Many of these cultivars showed increased yield with partial shading with semi-transparent solar photovoltaic (PV) systems. To further increase the efficiency of trellis-based growing systems, this study investigates novel low-cost, open-source, sustainable, wood-based PV racking designs for agrivoltaic applications. Design calculations are made to ensure these racks exceed Canadian building code standards, which with snow loads surpass those of most of the world. A complete bill of materials, fabrication instructions, and proof-of-concept prototypes are provided for three system topographies (sloped, T-shaped and inverse Y) along with economic analysis. In addition, to being cost competitive, the designs can act as trellis supports and be used for irrigation/fertigation purposes. The results indicate that these racking structures have enormous promise both agriculturally and energetically. If employed on only grape farms inside Canada, 10 GW of PV potential is made available, which is more than twice the total current installed PV in Canada. </div>
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.169 | 0.016 |
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; both teacher heads agree on what is shown here.
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".