liionpack: A Python package for simulating packs of batteries with PyBaMM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electrification of transport and other energy intensive activities is of growing importance as it \nprovides an underpinning method to reduce carbon emissions. With an increase in reliance on \nrenewable sources of energy and a reduction in the use of more predictable fossil fuels in both \nstationary and mobile applications, energy storage will play a pivotal role and batteries are \ncurrently the most widely adopted and versatile form. Therefore, understanding how batteries \nwork, how they degrade, and how to optimize and manage their operation at large scales is \ncritical to achieving emission reduction targets. The electric vehicle (EV) industry requires \na considerable number of batteries even for a single vehicle, sometimes numbering in the \nthousands if smaller cells are used, and the dynamics and degradation of these systems, as well \nas large stationary power systems, is not that well understood. As increases in the efficiency \nof a single battery become diminishing for standard commercially available chemistries, gains \nmade at the system level become more important and can potentially be realised more quickly \ncompared with developing new chemistries. Mathematical models and simulations provide a \nway to address these challenging questions and can aid the engineer and designers of batteries \nand battery management systems to provide longer lasting and more efficient energy storage \nsystems.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it