Validating Deposition Models in Disease: What Is Needed?
Bibliographic record
Abstract
To develop theoretical deposition models, assumptions are introduced to make the models computationally affordable. For this reason, experimental (in vivo) validation of such models is needed to give confidence to the assumptions being made. However, for an in vivo deposition experiment to be considered useful for validation of a model, a number of parameters must be measured in the experiment for input to the model. Ideally, these parameters would include time-dependent breathing flow rates during aerosol exposure, properties of the inhaled aerosol as a function of time during the breath (including particle size distribution, aerosol mass fraction, as well as hygroscopic properties, inhaled temperature and humidity if hygroscopicity is important), in addition to anatomical regional deposition data and detailed lung geometry measurements. Furthermore, because of the dependence of extrathoracic filtering on the inlet conditions at the mouth and the complexity of modeling deposition in this region, experimental data on the filtering properties of the mouth-throat are needed. Although some of the above parameters are impractical to measure with current experimental techniques, it would greatly aid the development of deposition models if as many of these parameters as possible were measured in future in vivo deposition experiments. Data exemplifying the importance of measuring the above parameters is discussed.
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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 | 0.120 | 0.231 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".