Bibliographic record
Abstract
It was with interest that I read a recent article in the International Journal of Sports Medicine by KE Lansley et al. entitled, “A ‘new’ method to normalize exercise intensity” (IJSM 2011; 32: 535–541) [ 1 ]. My interest was provoked with the topic area that was very similar to research that I performed during my masters and doctoral years in the late 1970’s and early 1980’s. Far too often in peer-reviewed scientific articles I see the phrase “to the best of our knowledge” used in the introduction to supposedly verify and validate the novelty of the rationale for the research. Unfortunately it sometimes seems that the thorough search for earlier publications is not as thorough as one would hope. Historically, Dr. Whipp was the external examiner for my doctoral research which included different studies that used the “aerobic” and “anaerobic” thresholds as a basis to explain differences in performance and adaptations to training. My research with my supervisor, Dr. James Skinner, was published in the Canadian Journal of Applied Sport Science [ 2 ] [ 4 ] (now published as the Applied Physiology, Nutrition and Metabolism Journal) and the International Journal of Sports Medicine [ 3 ] and later research using this concept was published in Medicine and Science in Sports and Exercise [ 5 ]. Specifically I would refer Drs. Lansley et al. to papers that used the aerobic threshold to establish training intensities [ 2 ] or to decrease the variability in blood lactate clearance during active recovery following high intensity exercise [ 3 ]. In addition, I would refer the authors of the current manuscript to examine papers that used both the aerobic and anaerobic thresholds to explain differences in performance [ 4 ] and blood lactate and acid-base responses [ 5 ]. I certainly would encourage the authors of the current paper in IJSM to continue to advocate the use of alternative methods to express exercise intensity. One option that we have explored recently is the use of thermal strain as the independent variable rather than %VO2max to explain cytokine and endocrine responses [ 6 ] [ 7 ]. In addition, I realize that it is not possible to have read and be cognizant of all of the literature on a particular topic and certainly the volume of literature dealing with gas exchange and blood lactate “thresholds”, critical power and the related methodology is immense. It was quite substantial when I was a doctoral student over 30 years ago so it is understandable that authors today are not aware of all of the literature that was published many years earlier. Perhaps the best advice would be to avoid the use of the phrase “to the best of our knowledge” since I would suspect that there are many topics where literature searches are perhaps not as thorough as they should be.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".