Does Leaf Micro-Morphology Influence The Recognition Of Particles On SEM Images?
Bibliographic record
Abstract
Scanning electron microscopy (SEM) remains a popular approach to determine the shape, size, density and elemental composition of particles collected on leaf surfaces, but the effect of leaf micro-morphology on particle counts is not very well known. In this study, leaves of sixteen urban plant species were examined for particle density in June and September 2016 using SEM. The investigated plant species differed in leaf micromorphology involving trichomes, raised stomata, epicuticular wax crystals and convex epidermal cells forming deep grooves between cells. The particle density on leaves of the investigated plant species was estimated by particle size fraction and leaf side. Particle density was significantly higher on the adaxial (AD) leaf side compared to the abaxial (AB) leaf side and higher for fine-particles than coarse-particles. The effect of trichome density on particle density of the AB and the AD leaf side was indicated to be significant and positive for both coarse and fineparticles in June but not in September. The successive repeated measurements elucidated that features constructing the topography of a leaf surface such as trichomes, stomata, and epidermal cells frequently contributed towards the edge enhancement effect, resulting in exaggerated particle counts. Besides, the mechanical drift contributed to the disparity in particle density measurements. Lastly, the reduction in particle density between successive measurements were imputed on the charging effect. These results enable us to suggest that in addition to characterization of micro-morphological features on a leaf surface, SEM will continue to be a useful approach for determining the particle: shape, size, elemental composition and density of the deposited particles. Nonetheless, the disparity in particle density measurements can occur due to abnormal particle recognition. Based on the results of September, we recommend that within-session successive repeated measurements (~ n 5) need to be performed to remove measurement uncertainties and obtain reliable quantitative data of particle counts using SEM
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".