To what extent does the environmental magnetism technique acts as a way of assessing the uptake of atmospheric particles by plants?
Bibliographic record
Abstract
<p>Numerous studies have already shown the possibility of tracing the sources, the<br>compositions, and the concentration of atmospheric pollutants deposited on plant<br>leaves. In environmental geochemistry, inter-element and isotope ratios from<br>chemical element assays have been used for these purposes. Alternatively,<br>environmental magnetism represents a quick and inexpensive asset that is<br>increasingly used as a relative indicator for concentrations of air pollutant on bio<br>accumulator surfaces such as plants. However, a fundamental issue is still pending:<br>Do plants in urban areas represent a sink for fine particles that is sufficiently effective<br>to improve air quality? This is a very topical issue because some studies have shown<br>that the foliage can trap fine particles by different dry deposition processes, while<br>other studies based on CFD models indicate that plant hedges in cities can hinder<br>the atmospheric dispersion of pollutants and therefore increase pollution at the level of<br>emission sources such as traffic. To date, no consensus was made because several<br>factors not necessary well known must be taken into account, such as, PM<br>concentration and size, prevailing wind, surface structures, epicuticular wax, to<br>mention just a few examples. A first step toward the understanding of the impact of<br>urban greens on air quality is the precise determination of the deposition velocity (Vd)<br>parameter. This latter is specific for each species and it is most of the time<br>underestimated in modeling-based studies by taking standard values.<br>In that perspective, we built a wind tunnel (6 m long, 86 cm wide and 86 cm high) to<br>perform analogical experiments on different endemic species. All parameters are<br>controlled, i.e, the wind speed, the nature and the injection time of pollutants (Gasoline<br>or Diesel exhausts, brakes or tires dust, etc…). We can provide the PM concentrations<br>upwind and downwind of natural reconstituted hedges by two dustmeters (LOACs -<br>MétéoModem). Beforehand, parameters such as the hedge resistance (%) or the leaf<br>area index (LAI) have been estimated for each studied specie to allow comparability<br>between plants removal potential. The interest would ultimately combine PM<br>concentration measured by size bins from the LOACs with magnetic measurements<br>(ARM, IRM100mT, IRM300mT and SIRM) of plant leaves. The idea is to check whether it<br>would be possible to precisely determine in situ the dust removal rate by urban greens<br>with environmental magnetism measurements. Up to now, we have carried out on<br>different endemic species such as Elaeagnus x ebbingei leaves and Mediterranean<br>pine needles, the results of which will be presented.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".