Energy expenditure characteristics of weight lifting: 2 sets to fatigue
Bibliographic record
Abstract
We investigated the work performed and energy expenditure characteristics within and among 2 sets of the bench press at 70%, 80%, and 90% of 1 repetition maximum (1RM). For both sets fatigue was the end point. We asked: do multiple sets affect subsequent work output along with aerobic, anaerobic, and excess postexercise oxygen consumption (EPOC) contributions? Ten males participated. Work was significantly less for the 2nd set within the 70% and 80% protocols, but not the 90% protocol. Anaerobic (glycolytic) energy expenditure was less for the 2nd set within all protocols. However, within all protocols, the work / energy expenditure ratio was not different between sets. Overall work was significantly different among protocols, becoming less as the weight lifted was increased: 70%, 637.1 ± 122.4 J; 80%, 512.4 ± 93.4 J; 90%, 324.7 ± 92.6 J (p < 0.001). EPOC was not different among protocols after the 1st set, 2nd set, or combined overall. Moreover, the overall EPOC did not correlate with overall work performed (r = 0.31, p = 0.11). EPOC overall did correlate with aerobic (r = 0.68, p < 0.001) and anaerobic (r = 0.65, p < 0.001) energy expenditures. In terms of a work / energy expenditure ratio, the least amount of completed work at 90% 1RM required greater energy expenditure as compared with 70% and 80% because of an EPOC that is similar for all. As more work is completed (i.e., lower weight, more repetitions), aerobic and anaerobic exercise energy expenditures appear to increase accordingly, yet absolute EPOC remains essentially unchanged, contributing less to the overall energy expenditure.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".