Seasonal Changes in Atmospheric Corrosivity
Bibliographic record
Abstract
Abstract There have been several attempts to correlate environmental and site-specific variables with atmospheric corrosivity, including the PACER LIME algorithm and the ISO 9223 classification of corrosive atmospheres. However, seasonal variations in corrosivity at the locations tested in this study cannot be readily explained by either of these approaches. The monthly or seasonal corrosivity variations do not track well with monthly variations in time-of-wetness (TOW) or SO2 concentration. It is possible that the approach of averaging TOW, SO2, or chloride deposition rate over a year- or month-long period hides shorter-term events that are truly influential. Therefore, while seasonal or month-long measurements of corrosivity provide more insight than yearlong measurements, it seems necessary to reduce the time scale down to days or hours before clear correlations can be made between corrosivity and corrosion drivers. Also, in urban settings where roads are de-iced, it is necessary to measure chloride deposition rates since it is a significant corrosion driver. Another conclusion is that accounting for the effects of insolation from the sun and convection on the surface temperature of an exposed metal provides more insights than time-of-wetness calculations, which relies on relative humidity alone.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".