Bibliographic record
Abstract
This NeuroLibre Reproducible preprint is an interactive tutorial on quantitative T1 mapping MRI.It is an interactive version of two subsections of the chapter "Quantitative T1 and T1r Mapping" in the book Quantitative Magnetic Resonance Imaging (Boudreau et al., 2020). Exclamat NOTEThe following section in this document repeats the narrative content exactly as found in the corresponding NeuroLibre Reproducible Preprint (NRP).The content was automatically incorporated into this PDF using the NeuroLibre publication workflow (Karakuzu et al., 2022) to credit the referenced resources.The submitting author of the preprint has verified and approved the inclusion of this section through a GitHub pull request made to the source repository from which this document was built.Please note that the figures and tables have been excluded from this (static) document.To interactively explore such outputs and re-generate them, please visit the corresponding NRP.For more information on integrated research objects (e.g., NRPs) that bundle narrative and executable content for reproducible and transparent publications, please refer to DuPre et al. (2022).NeuroLibre is sponsored by the Canadian Open Neuroscience Platform (CONP) (Harding et al., 2023).
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".