Demystification of Arc-Fault Circuit-Interrupters (AFCIs) – PART I: Beginning of the Odyssey
Bibliographic record
Abstract
Residential fires of electrical origin have been a major concern for a long time. A fire can be initiated by excessive current (due to an overload or a short circuit), or arcing current. Therefore, both Canadian Electrical Code (CE Code) Part I and the National Electrical Code (NEC) require the installation of overcurrent protection devices (OCPDs) to detect and clear excessive current. Conversely, arcing current is too low for OCPDs to detect. It could take an electric arc, minutes, days, weeks, months, or even years to initiate a fire. Therefore, a new solution was required for detecting those slowly developing arcs. Thus, Arc-fault Circuit-Interrupters (AFCls) were born. AFCls are capable of detecting an arcing condition (while still developing) and de-energizing the circuit before the arcing circuit ignites. AFCls have been a hot topic creating quite a bit of controversy in the recent NEC review cycles. It is the authors' opinion that this controversy stems from a lack of clear understanding of AFCls operation, available technologies, and their capabilities. This paper is the first of two papers attempting to clarify the confusion surrounding AFCls, their applications, and success in making an impact on home electrical fires.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".