Premium Efficiency Motors
Bibliographic record
Abstract
0 94.5 95.4 95.4 94.5 95.4 95 95 94.5 300.0 95.0 95.4 95.4 94.5 95.4 95.4 95 0 350.0 95.0 95.4 95.4 94.5 95.4 95.4 95 0 400.0 95.4 95.4 0.0 0.0 95.4 95.4 0 0 450.0 95.8 95.8 0.0 0.0 95.4 95.4 0 0 500.095.8 95.8 0.0 0.0 95.4 95.8 0 0 HP ODP TEFC Annex 1. NEMA MG-1 Table 12-11 Full-Load Efficiencies of Energy Efficient Motors (EPAct) www.intechopen.com95 94.5 93.6 95 94.5 75 93.6 95 94.5 93.6 95.4 94.5 100 93.6 95.4 95 94.1 95.4 95 125 94.1 95.4 95 95 95.4 95 150 94.1 95.8 95.4 95 95.8 95.8 200 95 95.8 95.4 95.4 96.2 95.8 250 95 95.8 95.4 95.8 96.2 95.8 300 95.4 95.8 95.4 95.8 96.2 95.8 350 95.4 95.8 95.4 95.8 96.2 95.8 400 95.8 95.8 95.8 95.8 96.2 95.8 450 95.8 96.2 96.2 95.8 96.2 95.8 500 95.8 96.2 96.2 95.8 96.2 95.8 HP ODP TEFC Annex 2. NEMA MG-1 Table 12-12 Full-Load Efficiencies for 60 Hz NEMA Premium Efficient Electric Motors Rated 600 Volts or less www.intechopen.com150 94.5 95.0 95.0 110 150 94.3 94.5 94.3 150 200 95.0 95.0 95.0 132 175 94.6 94.7 94.6 185 250 95.4 95.4 95.0 160 215 94.8 94.9 94.8 200 270 95.4 95.4 95.0 200 270 95.0 95.1 95.0 260 350 95.4 95.4 95.0 260 350 95.0 95.1 95.0 300 400 95.4 95.4 95.0 300 400 95.0 95.1 95.0 335 450 95.4 95.4 95.0 335 450 95.0 95.1 95.0 375 500 95.4 95.4 95.0 375 500 95.0 95.1 95.0 125 95.0 95.2 94.9 110 150 95.2 95.4 95.1 132 175 95.4 95.6 95.4 160 215 95.6 95.8 95.6 200 270 95.8 96.0 95.8 260 350 95.8 96.0 95.8 300 400 95.8 96.0 95.8 335 450 95.8 96.0 95.8 375 500 95.8 96.0 95.8 How to referenceIn order to correctly reference this scholarly work, feel free to copy and paste the following: M. Benhaddadi, G.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.224 | 0.118 |
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".