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Record W4253596979 · doi:10.1249/mss.0000000000001820

Response

2019· letter· en· W4253596979 on OpenAlexaff
Daniel A. Keir, Silvia Pogliaghi, Juan M. Murias

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2019
Typeletter
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of CalgaryUniversity Health Network
Fundersnot available
KeywordsSteady state (chemistry)Intensity (physics)Boundary (topology)MathematicsRepresentation (politics)PhysicsStatisticsMathematical analysisLawChemistryQuantum mechanicsPolitical science

Abstract

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Dear Editor-in-Chief, In their letter, Marwood et al. conclude that critical power (CP) rather than maximal lactate steady-state (MLSS) offers the best representation of the maximal metabolic steady-state (i.e., critical intensity). While defending this position, the authors claim: That we previously proposed (1) “CP…overestimates the heavy–severe boundary.” This is a distortion of our views. Although overestimations predominated in Mattioni Maturana et al. (2), we contend that CP can both overestimate and underestimate the maximal metabolic steady-state depending on factors, such as the type of test, model, and fitting strategy used (3–5). In fact, an approximately 5% margin of error in CP estimation is acknowledged by others who support CP as the heavy–severe intensity boundary (6). While acknowledging that MLSS is itself an estimate of the maximal metabolic steady-state, in our view, MLSS is superior to CP testing because it simultaneously verifies whether the physiological responses conform to those expected at the critical intensity. Using MLSS “as the primary marker of the heavy–severe intensity boundary…is ironic given the arbitrary and highly liberal definition of MLSS.” There is no irony in using a delta change of 1 mmol·L−1 between 10 and 30 min as the criteria for a stable [La]. This is a well-established model. How liberal or conservative this measure needs to be can be debated, but normal measurement variability must be considered. Nevertheless, providing a physiological validation of the critical intensity of exercise is always more appropriate than accepting a model parameter estimate without any verification. “Based on available evidence…we contend that CP, when appropriately determined, is most representative of the upper limit of the metabolic steady state” This observation simply ignores several recent lines of evidence (2,3,7) and even common sense (i.e., how can metabolic steady-state be assumed without measuring metabolic responses to exercise?). We have discussed this topic in detail elsewhere (8). In short, Poole et al. (6) defined CP as “the highest intensity that can be sustained for a prolonged time solely by oxidative energy provision.” In this definition, exercise at CP does not draw upon anaerobic metabolism. Thus, progressive depletions in phosphocreatine and accumulations of [La] are not evident with time, which minimizes metabolic and acid–base disturbance and delays the initiation of fatigue. Therefore, the physiological responses expected at CP are those of MLSS. Any differences between these two indices simply relate to imprecisions inherent with the methods used for their determination. Although we believe that CP is a good approximation of the heavy–severe boundary, the clear limitations of this approach (2–4) make its use for research purposes inadequate, unless physiological validation is conducted to confirm metabolic stability at CP. To conclude, too often it is assumed that the model output estimate of CP reflects the true critical intensity of exercise (i.e., the heavy–severe boundary) despite compelling evidence that this is not always the case (2,3,7). Both MLSS and CP testing have inherent limitations, but in the absence of physiological verification, CP testing carries a greater degree of predictive uncertainty.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.091
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0170.018
Insufficient payload (model declined to judge)0.0910.066

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.

Opus teacher head0.016
GPT teacher head0.281
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

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