Bibliographic record
Abstract
Lung volumes (FVC1, FEV1.0 and peak flow) were measured in 403 men and 561 women volunteers for fitness testing, using the SRL automated spirometer system. Average scores for this population were 5--10% higher than predicted from age, height and sex using either the formulae inherent in the SRL computer or standards proposed by Anderson et al. [1968] for the Toronto population; existing standards may thus underestimate respiratory potential. Lung function data showed a dose-dependent decrease within the category of cigarette smokers, but there was no significant difference between average results for smokers and non-smokers. Multivariate analysis showed significant contributions of lean mass and obesity to the overall description of lung volumes; however, effects were not large enough to justify incorporation of such variables into routine prediction equations. Positive responses to the respiratory section of the Cornell Medical Index were in several instances associated with below expected lung volumes. The most consistent response was to the question 'do you suffer from asthma?' Although the average effect was significant, the magnitude of response (10--20%) would have been overlooked in individual testing; this suggests that there may be more scope for pulmonary screening through the improvement of questionnaires than through the purchase of expensive electronic spirometers.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".