Disentangling Natural and Anthropogenic Sources of Dust Deposition to a Montane Ecosystem at San Jacinto Peak, Southern California
Bibliographic record
Abstract
Abstract The composition and flux of mineral dust are largely driven by the entrainment and transport of sediment from both natural and human sources, resulting in varying ecological impacts at the deposition site. To investigate the influence of natural and human sources of dust in montane environments, we measured dust composition and deposition rate on San Jacinto Peak in Southern California from 2019 to 2022 at six sites spanning 2,462 m in elevation and 20 km in distance. We find unique interannual variations in fine dust (0.2–30 μm) flux and chemical composition between sites. The greatest average dust flux occurs during July–November (0.14–3.50 g m −2 y −1 ), followed by March–July (0.24–4.07 g m −2 y −1 ) and is lowest during November–March (0.29–2.76 g m −2 y −1 ). Wildfires led to significant increases in dust flux, with the highest dust flux occurring at the lowest elevation site following the 2020 Snow Creek fire. Greater enrichment of metals and depletion of rare earth elements at higher relative to lower elevations indicate spatial and temporal variability in dust sources, consistent with variations in natural and anthropogenic inputs. Positive matrix factorization (PMF) indicates that high elevation sites on average receive a higher proportion of anthropogenic dust input (64%–75%), whereas low‐elevation sites receive a higher proportion of alluvium and local rock inputs (35%–63%), particularly on the north side of the mountain. This study highlights the complexity of interannual dust deposition in mountain environments and the modulation of dust flux and composition by anthropogenic activity and wildfire.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".