Moving the Open-Source Broadly Reconfigurable and Expandable Automation Device (BREAD) Towards a Supervisory Control and Data Acquisition (SCADA) System
Bibliographic record
Abstract
While the free and open-source Broadly Reconfigurable and Expandable Automation Device (BREAD) has demonstrated functionality as an inexpensive replacement for many commercial controllers, some aspects of its design require updating to make it more aligned with commercial supervisory control and data acquisition (SCADA) systems. Some of these updates to BREAD for version 2 included improvements to the mechanical design for stability with an alignment cover, rail mounting with Deutsche Institut für Normung (DIN) rail clips, ESP32 Loaf Controller with local wireless connectivity, and open-source web browser-based software control. These updates were validated by comparing BREAD v2 to an existing commercial controller used for airline-based pH control for industrial seaweed production. BREAD v2 was integrated into an electrical enclosure complete with pH probes, CO2 lines, solenoid valves, and a power supply. After comparing the two approaches, BREAD v2 was found to be more precise by roughly a factor of five, and less expensive by a factor of three than proprietary systems, while also offering additional functionality like data logging and wireless monitoring. Although able to match or beat specific functions of SCADA systems, future work is needed to transform BREAD into a full SCADA system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".