Cooling at Tokyo 2020: the why and how for endurance and team sport athletes
Bibliographic record
Abstract
The Tokyo 2020(1) Olympics are expected to be the hottest in modern history,1 resulting in much conjecture within the literature.2–5 Long-term (~10 to 14 days) heat acclimation/acclimatisation (HA) is the gold-standard strategy to protect against heat-mediated performance decrements and exertional heat illnesses (EHI).6 Short-term heat reacclimation (~5 days), proximal to competition, can also be incorporated within athlete training and taper programmes, complimenting the earlier long-term HA. This approach allows the balance of training/load and HA agendas within the often time poor and logistically challenging elite sport environment.5 7 8 With the assumption that athletes arrive robustly heat acclimated/acclimatised to Tokyo 2020(1), practitioners have a variety of precooling, during(mid) and postcooling event interventions to consider on competition day – that are complimentarily to – rather than instead of HA.8 In brief, these can include various combinations of: (i) internal (ice slurry ingestion, cold water ingestion, etc) and external (any cold fluid, medium or air source the body is immersed or exposed to) body cooling interventions to reduce body tissue temperatures [eg, core (Tc), muscle (Tmu) and skin (Tsk) temperature (see figure 1 for summary)]8–10; (ii) interventions to evoke local cooling sensations (eg, menthol mouth rinse) which could be favourably interpreted (ie, their perception) by higher brain centres without altering Tc11 and (iii) titration of competition warm-up procedures and/or alterations in pacing, tactics and/or strategy. At the recent 2019 IAAF World Athletics Championships (Doha, Qatar), LT …
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".