Prediction of the Superparamagnetic Limit for Magnetic Storage Medium Using Artificial Neural Networks
Bibliographic record
Abstract
In this study, computational techniques based on artificial neural networks for three models were used for training sets, Né el's relaxation time, particle Length and the decay of magnetization were used to perform superparamagnetic calculations for Co3Pt and FePt typical magnetic storage medium.The magnetic medium's magnetisation stability was studied using the thermal stability coefficient by determining the Né el relaxation time.The superparamagnetic limit was discovered to determine the size of the magnetic particle that can maintain its magnetization for over 10 years, larger particles (8 nm 3 for FePt and 64 nm 3 for Co3Pt) are required.The decay of magnetization occurs when the thermal stability factor exceeds 40. the effect of changing the neural network's parameters on its performance was examined.The results demonstrated the high sensitivity of the designed neural network's response, which relies on the backpropagation technique to change these parameters.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".