Bibliographic record
Abstract
Electric transformers are situated where electric power is used – frequently, close to people. Due to their hum and the noise of cooling fans they may cause intrusive noise levels. A noise impact assessment (NIA) is therefor a useful tool to predict noise levels near third party stakeholders, to assess their acceptability and to prevent future noise problems. The sound power level (PWL) provides necessary information to be used in such an assessment or compare transformers. Typically, the PWL for transformers is calculated from measurements using standards such as IEEEI standard C57.12.90-2010, “IEEE Standard Test Code for Liquid-Immersed Distribution, Power and Regulating Transformers”. That standard requires an elaborate number of individual measurements around a transformer and results in a single-number PWL, without taking potential directivity into account. The method may therefore not be the most suitable to provide the necessary data for the purpose of assessing the noise impact from transformers. This paper aims to compare the measurement procedure and results according to IEEEI standard C57.12.90-2010 to other relevant standards such as ISO 3744:2010 “Determination of sound power levels and sound energy levels of noise sources using sound pressure – Engineering methods for an essentially free field over a reflecting plane” or measurements at some distance, assuming the transformer as a point source. Henk de Haan, Eur. Ing. INCE Bd. Cert. dBA Noise Consultants Ltd henk@dbanoise.com (403) 836 8806
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".