Phosphorus Speciation in Atmospherically Deposited Particulate Matter and Implications for Terrestrial Ecosystem Productivity
Bibliographic record
Abstract
Chemical forms of phosphorus (P) in airborne particulate matter (PM) are poorly known and do not correlate with solubility or extraction measurements commonly used to infer speciation. We used P X-ray absorption near-edge structure (XANES) and 31 P nuclear magnetic resonance (NMR) spectroscopies to determine P species in PM collected at four mountain sites (Colorado and California). Organic P species dominated samples from high elevations, with organic P estimated at 65–100% of total P in bulk samples by XANES and 79–88% in extracted fractions (62–84% of total P) by NMR regardless of particle size (≥10 or 1–10 μm). Phosphorus monoester and diester organic species were dominant and present in about equal proportions, with low fractions of inorganic P species. By comparison, PM from low elevation contained mixtures of organic and inorganic P, with organic P estimated at 30–60% of total P. Intercontinental PM transport determined from radiogenic lead (Pb) isotopes varied from 0 to 59% (mean 37%) Asian-sourced Pb at high elevation, whereas stronger regional PM inputs were found at low elevation. Airborne flux of bioavailable P to high-elevation ecosystems may be twice as high as estimated by global models, which will disproportionately affect net primary productivity.
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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 | 0.001 |
| 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".