Battery Development for Stop-Start Application in Brazilian Market
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">There is a growing worldwide concern regarding the environmental aspects related to the performance of a corporation and its products, whether by consumer demand or government requirements. The constant pressure for innovations and improvements related to sustainable development are current issues in everyday life of any institution that seeks to consolidate a position of acceptance and competitiveness in the global market.</div><div class="htmlview paragraph">The automotive industry is one of the markets more involved and challenged to the demand of the environmental requirements in regards the limits of pollutant emissions and consequently fuel consumption.</div><div class="htmlview paragraph">The European and North America vehicles already have more electrical content inside (either related to safety and comfort or even needs related to weather), which results in significantly higher consumption levels than traditionally observed in Brazil's application. This divergence challenges the Brazilian automakers and their suppliers to develop appropriate and local solutions to the demands of emerging markets.</div><div class="htmlview paragraph">This paper aims to present the studies performed with several Lead Acid battery technologies available for application in Stop-Start vehicles in the Brazilian market. Brazil is the largest automobile market in Latin America and is positioned as 4th largest in the world market (according to 2011, Jatto Dynamics). With significant growth prospects in the coming years, Brazil has consolidated not only as consumers, but as reference in development of solutions to suit the reality of emerging countries. The Brazilian environmental regulations trends to international level, this challenges the automakers to adopt new emerging technologies and adapts others to ensure a good balance of cost-benefit.</div></div>
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".