Blood flow and muscle oxygen uptake at the onset and end of moderate and heavy dynamic forearm exercise
Bibliographic record
Abstract
We hypothesized that forearm blood flow (FBF) during moderate intensity dynamic exercise would meet the demands of the exercise and that postexercise FBF would quickly recover. In contrast, during heavy exercise, FBF would be inadequate causing a marked postexercise hyperemia and sustained increase in muscle oxygen uptake (VO(2musc)). Six subjects did forearm exercise (1-s contraction/relaxation, 1-s pause) for 5 min at 25 and 75% of peak workload. FBF was determined by Doppler ultrasound, and O(2) extraction was estimated from venous blood samples. In moderate exercise, FBF and VO(2musc) increased within 2 min to steady state. Rapid recovery to baseline suggested adequate O(2) supply during moderate exercise. In contrast, FBF was not adequate during heavy dynamic exercise. Immediately postexercise, there was an approximately 50% increase in FBF. Furthermore, we observed for the first time in the recovery period an increase in VO(2musc) above end-exercise values. During moderate exercise, O(2) supply met requirements, but with heavy forearm exercise, inadequate O(2) supply during exercise caused accumulation of a large O(2) deficit that was repaid during recovery.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".