Linear combination of multiecho data: Short <i>T</i><sub>2</sub> component selection
Bibliographic record
Abstract
The myelin sheath, which is wrapped around the axons in the brain, can be affected by many diseases, resulting in cognitive and physical disability. Other work showed water in the myelin sheath has a T2 approximately 15 ms. The current standard technique to estimate the fraction of myelin water in vivo is to collect multiecho data and fit the decay curves using a nonnegative least-squares (NNLS) algorithm. A new algorithm was developed to calculate optimized coefficients which were used to linearly combine multiecho data to estimate the myelin water signal. A set of simulations showed the new technique was accurate over a broad range of myelin water signal. The myelin water fraction from brain regions in scans from five volunteers, estimated by the linear combination method, agreed with the myelin water fraction estimated by the standard technique. The strength of the new technique is that the linear combination does not assume an underlying T2 model and is 20,000 times faster than NNLS.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".