Improved solutions to a TEAM problem for multi‐objective optimisation in magnetics
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
New solutions to a recently proposed benchmark TEAM problem for Pareto optimisation are presented. In the benchmark, an air‐cored solenoid of small size, which can be used, for example, for magnetic fluid hyperthermia, is considered. Two shape optimisations of the solenoid are proposed in the benchmark: synthesising a uniform magnetic field in a control region, considering also a sensitivity function (Problem 1) or synthesising a uniform magnetic field, simultaneously minimising the power losses (Problem 2). The benchmark is solved by means of three different nature‐inspired algorithms and a genetic one, namely micro biogeography‐inspired algorithm, wind‐driven optimisation, and the cuckoo search, taking the genetic algorithm NSGA‐II as a reference, because all these methods have proven to be effective in solving multi‐objective optimisation problems.
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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 | 0.003 |
| 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 it