The use of modeling as an alternative magnetic resonance image technique
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
M.R. Smith et al. (see IEEE Trans. Biomed. Imaging, vol.BM5-3, no.3, p.132-139, 1986) and E.M. Haacke et al. (see IEEE Trans. Acoust., Speech, and Signal Proc., to appear) have reported success with modeling techniques that overcome the problem of reduced data sets in MR data (magnetic resonance) imaging. The present authors detail the assumptions made by the Smith and Haacke reconstruction methods and their success in modeling MR data. By taking into account experimental phase distortions, they generate an improved model that makes the basic assumptions of the above models more realistic. The authors present the theory behind determining phase corrections from the gathered data and show the effect of applying the modified algorithms. Experimental results confirm that the correction of the phase distortions reduces the required model order of both the Smith and Haacke methods.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.000 | 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 | 0.000 | 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