Elección de los parámetros que intervienen en un modelo de regresión local
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
Micro-computed tomography (micro-CT) is used routinely to quantify skeletal tissue mass in small animal models. Our goal was to evaluate repeated <i>in vivo</i> micro-CT imaging for monitoring whole-body composition in studies of growth and aging in mice. Male mice from 2 to 52 weeks of age were anesthetized and imaged using an eXplore Locus Ultra and/or eXplore speCZT scanner. Images were reconstructed into 3D volumes, signal-intensity thresholds were used to classify each voxel as adipose, lean or skeletal tissue, and tissue masses were calculated from known density values. Images revealed specific changes in tissue distribution with growth and aging. Quantification showed biphasic increases in total CT-derived body mass, lean and skeletal tissue masses, consisting of rapid increases to 8 weeks of age, followed by slow linear increases to 52 weeks. In contrast, bone mineral density increased rapidly to a stable plateau at ~ 14 weeks of age. On the other hand, adipose tissue mass increased continuously with age. A micro-CT-derived total mass was calculated for each mouse and compared with gravimetrically measured mass, which differed on average by < 3%. Parameters were highly reproducible for mice of the same age, but variability increased slightly with age. There was also good agreement in parameters for the same group of mice scanned on the eXplore Locus Ultra and eXplore speCZT systems. This study provides reference values for normative comparisons; as well, it demonstrates the usefulness of <i>in vivo</i> single-energy micro-CT scans to quantify whole-body composition in high-throughput studies of growth and aging in mice.
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.
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 | 0.001 | 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) | 0.001 | 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