A Biomedical Design of a Femtotesla Ferromagnetic Detector with Quasi-Super Conductor
Bibliographic record
Abstract
This paper presents a flexible femto-tesla detector design using multi-level cascade modules (quantum magnetic chips, semiconductor cooling, graphene superconductors, power supply, and control circuits). The quantum magnetic chip could be any quantum effect-based chip such as tunnel magnetoresistance, superconducting quantum interference devices, spin exchange relaxation-free, optically pumped magnetometers, semiconductor cooling device could be made of bismuth telluride, lead telluride, silicon–germanium, and bismuth antimonide alloys with copper or graphene coated ceramic plate, soft version is preferable to prevent long term cracking issue, the superconductor could be zero resistance based or Meissner effect based, critical temperature high one is preferable, such as graphene, quasi superconduct like Ohno Continuous Casting (CCC) is acceptable as well, power supply and control circuits must be extreme low noise made with the latest chip technology like silicon carbide and silicon nitride. Such design is mainly meant for educational usage. The lower cost is the main design goal. Its magnetic focusing lens combines semiconductors with room-temperature quasi-superconductors. A tapered superconducting disk with a central elliptical hole concentrates magnetic flux by repelling field lines toward the hole, amplifying field strength. Civilian applications include detecting biological magnetism, say, monitoring the student attention level during the study, diagnosing Alzheimer’s and depression in humans/pets. The high-end military uses involve long-range detection of stealth submarines, carriers, tanks, and stealth aircraft. The main challenge of designing such a system is to understand the environment magnetic noise fluctuation patterns, as such, we have conducted short and long term measurements to catch the effect of Moon cycle on the background noise, these data and analysis will allow us to design an advanced Karman filter to remove the Moon noise, to see femto-Tesla variation in a more accurate design.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".