Particle capture efficiency of different-aged needles of Norway spruce under moderate and severe drought
Bibliographic record
Abstract
Trees can remove particulate matter from the atmosphere, improving air quality and providing ecosystem services. Particle removal capacity is known to differ between tree species, but the influence of environmental factors on the removal capacity is still unclear. In this study, we measured particle capture efficiency (Cp) of Norway spruce (Picea abies (L.) Karst.) in wind tunnel experiments under three watering treatments (well watered, moderate drought, and severe drought) and determined needle characteristics (stomatal conductance and density, wax condition, and needle area) that affect particle uptake. Trees were exposed in the wind tunnel to 0.7 μm (geometric mean diameter) NaCl particles with a mass concentration of 1 mg·m −3 , and the Cp of the tree was determined for the current-year (C) and previous-year (C+1) needles. Overall, the Cp was significantly higher for C+1 needles than for C needles for all watering treatments. There was also a trend for higher Cp of C+1 needles of less watered trees, but this was not observed for C needles. We suggest that greater erosion of the wax layer of C+1 needles compared with C needles increases hydrophilicity of the C+1 needle surface and this, in interaction with low stomatal conductance, led to the higher Cp.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".