Bibliographic record
Abstract
RECOMMENDATIONS 1. Aerobic PA (30 - 60 minutes of moderate to vigorous intensity most days of the week) can be considered for adults who want to: a. achieve small amounts of body weight and fat loss (Level 2a, Grade B) b. achieve reductions in abdominal visceral fat (Level 1a, Grade A) and ectopic fat such as liver and heart fat (Level 1a, Grade A),even in the absence of weight loss c. favour weight maintenance after weight loss (Level 2a, Grade B) d. favour the maintenance of fat-free mass during weight loss (Level 2a, Grade B) e. increase cardiorespiratory fitness (Level 2a, Grade B)[9] and mobility (Level 2a, Grade B). 2. For adults living with overweight or obesity, resistance training may promote weight maintenance or modest increases in muscle mass or fat-free mass and mobility (Level 2a, Grade B) 3. Increasing exercise intensity, including high-intensity interval training (HIIT), can achieve greater increases in cardiorespiratory fitness compared with moderate-intensity aerobic activity, and reduce the amount of time required to achieve similar benefits (Level 2a, Grade B). 4. Regular PA, with and without weight loss, can improve many cardiometabolic risk factors in adults who have overweight or obesity, including: a. hyperglycaemia and insulin sensitivity (Level 2b, Grade B) b. high blood pressure (Level 1a, Grade B) c. dyslipidaemia (Level 2a, Grade B). 5. Regular PA can improve health-related quality of life, mood disorders (i.e. depression, anxiety) and body image in adults with overweight or obesity (Level 2b, Grade B).
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".