Bibliographic record
Abstract
Although training intensity is commonly adapted by modifying the relative load (e.g. %1RM), absolute velocities are also targeted to facilitate speed- and power-oriented training objectives. PURPOSE: Examine the variation in relative loads and relative velocities used to perform a bench press at 6 absolute velocities. METHODS: Thirty men completed three bench press tests: 1RM, max velocity with 2.5kg bar, and 6 sets of 4 reps with loads of 15-90% 1RM. Participants were instructed to lower and lift the bar as fast as possible. Mean and peak concentric barbell velocity was computed via a linear position transducer. The average mean velocity of each 4-rep set and the relative load lifted were used to create participant-specific regression equations that would capture each individual’s load-velocity relationship. These equations were then used to estimate the %1RM that would have been used to move the bar with the group’s mean velocity with loads of 15-90% 1RM). These “target” velocities were also expressed as a relative percentage of the maximum velocity (%Vmax) achieved by each participant during the 2.5kg test. The variation in %1RM for each velocity was described by the standard deviation and range amongst participants. A similar approach was used to estimate the %Vmax that would have been achieved using a range of loads (15-100% 1RM). RESULTS: Lower %1RM and higher mean velocities were associated with the largest variation in training intensity across participants (Table 1). CONCLUSION: Using specific absolute mean velocities as “targets” could result in substantial variation to the corresponding %1RM and %Vmax across a group of athletes. To accommodate the abilities of each performer, it may be important to use relative velocity targets.Table 1: Participants’ estimated %1RM and estimated %Vmax for 7 absolute velocities and relative loads, respectively. Data were computed using the participant-specific regression equations, and are expressed as a mean, standard deviation (SD) and range.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".