Bibliographic record
Abstract
Magnetic encoding is currently widely employed in cheques, transaction cards, access cards and bank notes because of its robustness, economy, security, and ease of updating coded information. Coded magnetic information is currently read using either inductive metal-in-gap (MIG) or magnetoresistive (MR) heads.1)Due to various loss mechanisms, the signal-to-noise ratio of MIG heads peaks at around 100 kHz, decreasing rapidly at higher frequencies. The fabrication of both the MIG head2)as well as the accompanying signal processing circuitry3)is also non-trivial. MR heads provide higher SNR and signals that are independent of spatial frequency. They are however fragile, non-linear, and have a high temperature coefficient In cheques and bank notes, human-readable magnetic ink character recognition (MICR) characters are employed. Each MICR character has been designed to produce a distinct inductive head signal pattern. Unlike magnetic stripes, MICR characters signals are not binary when read using conventional read heads, resulting in increased read error rates. To avoid costly misreads, a closely spaced array of magnetic sensors can be utilized. Fabrication of read head arrays is, however, difficult in both technologies. A silicon magnetic sensor array fabricated using the charge-coupled device (CCD) technology has been designed to overcome these limitations. The magnetic sensor pixels are buried-channel MOSFET's with geometries designed to optimize magnetic sensitivity. The use of buried-channel, as opposed to surface-channel, MOSFET's results in enhanced sensitivity, lower noise, and higher signal resolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".