Model-based exploration of the drivers of mountain cold-trapping in soil
Bibliographic record
Abstract
A pollutant is said to undergo mountain cold-trapping if it is found at higher concentrations in a surface medium (soil, snow, foliage) high on a mountain, where it is colder, than in the same medium lower on the mountain. The processes that lead to mountain cold-trapping in soil were explored for a set of hypothetical Perfectly Persistent Pollutants (PPPs) by varying several environmental parameters in a fugacity based fate and transport box model. These parameters were: the spatial scale of the mountain; the rate and location of rain; the amount of particles in the atmosphere; the presence and magnitude of the upslope temperature gradient. The relative potential of each hypothetical PPP to exhibit mountain cold-trapping was expressed in terms of its Mountaintop Contamination Potential (MCP). The PPPs with the highest MCPs were those that neither were deposited from the atmosphere to the surface in the lower zones in the model nor left the model domain without being deposited at all. The simulations revealed that under most conditions wet-gaseous deposition is the biggest driver of mountain cold-trapping in soils, and its effects are greatly enhanced by large negative temperature gradients and increased precipitation upslope. Dry-gaseous and wet-and-dry-particle deposition processes cause similar PPPs to exhibit mountain cold-trapping, and the contributions to MCP by the dry processes are of the same magnitude as wet-particle deposition. Dry gaseous deposition alone is insufficient to cause mountain cold-trapping in soils under any conditions modelled here. Those measuring organic contaminants in mountains should expect to find that mountains with different climates cold-trap different pollutants, and that some mountains may not exhibit upslope enrichment of any species.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".