Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The development of a Powertrain Matching Analysis Tool (PMAT) addresses the challenge of matching the powertrain hardware and control strategy to specific vehicle attributes and driver applications for improved overall vehicle system efficiency. PMAT consists of a reverse tractive road load demand model and a dynamic optimization algorithm developed in MATLAB® and Simulink®. The reverse tractive road load demand model propagates the required wheel torque and speed derived from vehicle speed and road grade through the powertrain system to determine the required fuel flow for various states. The control strategy is treated as a multi-stage, multi-dimension decision process, where dynamic programming is applied to find an optimal control policy that minimizes the accumulated fuel flow over a drive cycle. PMAT is used to assess and develop transmission shift and lock-up control strategies, evaluate powertrain hardware configurations, and establish design criteria. PMAT can also virtually optimize multi-displacement cylinder deactivation system (MDS) operation taking gear and clutch control interaction effects into account. The advantages of the PMAT approach are demonstrated and key system integration concepts are revealed.</div></div>
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".