A Computer Architecture for the Automatic Design of Modular Systems With Application to Photovoltaic Reverse Osmosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Systems such as electronics, cars, computers, and robots are assembled from modular components for specific applications. Photovoltaic reverse osmosis (PVRO) systems, which can be custom-tailored for the water demands and solar properties of particular communities, are an important potential application of modular systems. Clearly, to be financially viable, such systems must be assembled from commercially available components and subsystems (modules). Designing a system from modular components for a specific application is not simple. Even for a relatively small inventory of modular components, the number of possible system configurations that exist is extremely large. For a small community, determining the best system configuration is an overwhelming task due to lack of expertise. This paper presents a modular design architecture that can be implemented on a laptop so nonexperts can configure systems from modular components. The method uses a hierarchy of filters, which can be provided from an expert system, to limit the large design space. Optimization methods and detailed models are then used to configure the location-specific system from the reduced design space. The method is applied here to community-scale PVRO systems and example cases demonstrate the effectiveness of the approach.
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.
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 | 0.001 | 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) | 0.000 | 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 it