Bibliographic record
Abstract
Gravity is critical for maintaining healthy bones since it causes loads that lead to bone strengthening and remodelling, particularly in weight-bearing bones such as those of the lower limbs. In contrast, reduced loading—such as during spaceflight—leads to bone resorption and loss of structure. Phosphate in the diet also plays an essential role in bone metabolism; while adequate intake supports healthy bone growth, deficiency and excess both compromise bone quality. This study investigates the interaction between mechanical unloading and dietary phosphate level on bone strength in microgravity. A rat model is used for the experiment, with the choice made in terms of accepted research standards and the similarity of rats to humans in terms of comparable bone remodelling response. Three experimental groups are male, female, and ovariectomized (OVX) female rats, the latter of which simulates postmenopausal status with estrogen deficiency, which plays a role in bone resorption. These groups are further divided into hindlimb-loaded and unloaded, and normal and high phosphate diet. (12 groups in total) Previous study on bone volume fraction and bone mineral density under space-like simulation indicated that an increased phosphate diet aggravates the bone loss over usual phosphate consumption. As a follow-up to these experiments, mechanical testing through 4-point bend tests and Finite Element Analysis (FEA) are being conducted to quantify the flexural strength of the bones. This research aims to obtain insight into the interactive influences of mechanical unloading and diet on bone health, with relevance towards the establishment of effective nutritional countermeasures for spaceflight. All procedures were approved by Animal Ethics. Image 1: 4-Point Bend Fixture and bone setup.
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 | 0.000 |
| 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 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".