Geographical Dependence of Open Hardware Optimization: Case Study of Solar Photovoltaic Racking
Bibliographic record
Abstract
Open-source technological development is well-known for rapid innovation and providing opportunities to reduce costs and thus increase accessibility for a wide range of products. This is done through distributed manufacturing, in which products are produced close to end users. There is anecdotal evidence that these opportunities are heavily geographically dependent, with some locations unable to acquire components to build open hardware at accessible prices because of trade restrictions, tariffs, taxes, or market availability. Supply chain disruptions during the COVID-19 pandemic exacerbated this and forced designers to pivot towards a la carte-style design frameworks for critical system components. To further develop this phenomenon, a case study of free and open-source solar photovoltaic (PV) racking systems is provided. Two similar open-source designs made from different materials are compared in terms of capital costs for their detailed bill of materials throughout ten locations in North, Central and South America. The differences in economic optimization showed that the costs of wood-based racks were superior in North America and in some South American countries, while metal was less costly in Central and South America. The results make it clear that open hardware designs would be best to allow for local optimization based on material availability in all designs.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".