Buoyancy-Corrected Gravimetric Analysis of Lightly Loaded Filters
Bibliographic record
Abstract
Numerous sources of uncertainty are associated with the gravimetric analysis of lightly loaded air filter samples (< 100 microg). The purpose of the study presented here is to investigate the effectiveness and limitations of air buoyancy corrections over experimentally adjusted conditions of temperature (21-25 degrees C) and relative humidity (RH) (16-60% RH). Conditioning (24 hr) and weighing were performed inside the Archimedes M3 environmentally controlled chamber. The measurements were performed using 20 size-fractionated samples of resuspended house dust loaded onto Teflo (PTFE) filters using a Micro-Orifice Uniform Deposit Impactor representing a wide range of mass loading (7.2-3130 microg) and cut sizes (0.056-9.9 microm). By maintaining tight controls on humidity (within 0.5% RH of control setting) throughout pre- and postweighing at each stepwise increase in RH, it was possible to quantify error due to water absorption: 45% of the total mass change due to water absorption occurred between 16 and 50% RH, and 55% occurred between 50 and 60% RH. The buoyancy corrections ranged from -3.5 to +5.8 microg in magnitude and improved relative standard deviation (RSD) from 21.3% (uncorrected) to 5.6% (corrected) for a 7.2 microg sample. It is recommended that protocols for weighing low-mass particle samples (e.g., nanoparticle samples) should include buoyancy corrections and tight temperature/humidity controls. In some cases, conditioning times longer than 24 hr may be warranted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".