Twin-cuvette measurement technique for investigation of dry deposition of O <sub>3</sub> and PAN to plant leaves under controlled humidity conditions
Bibliographic record
Abstract
We present a dynamic twin-cuvette system for quantifying the trace-gas exchange fluxes between plants and the atmosphere under controlled temperature, light, and humidity conditions. Compared with a single-cuvette system, the twin-cuvette system is insensitive to disturbing background effects such as wall deposition. In combination with a climate chamber, we can perform flux measurements under constant and controllable environmental conditions. With an Automatic Temperature Regulated Air Humidification System (ATRAHS), we are able to regulate the relative humidity inside both cuvettes between 40 and 90 % with a high precision of 0.3 %. Thus, we could demonstrate that for a cuvette system operated with a high flow rate (> 20 L min −1 ), a temperature-regulated humidification system such as ATRAHS is an accurate method for air humidification of the flushing air. Furthermore, the fully automatic progressive fill-up of ATRAHS based on a floating valve improved the performance of the entire measurement system and prevented data gaps. Two reactive gas species, ozone (O 3 ) and peroxyacetyl nitrate (PAN), were used to demonstrate the quality and performance of the twin-cuvette system. O 3 and PAN exchange with Quercus ilex was investigated over a 14 day measurement period under controlled climate chamber conditions. By using O 3 mixing ratios between 32 and 105 ppb and PAN mixing ratios between 100 and 350 ppt, a linear dependency of the O 3 flux as well as the PAN flux in relation to its ambient mixing ratio could be observed. At relative humidity (RH) of 40 %, the deposition velocity ratio of O 3 and PAN was determined to be 0.45. At that humidity, the deposition of O 3 to the plant leaves was found to be only controlled by the leaf stomata. For PAN, an additional resistance inhibited the uptake of PAN by the leaves. Furthermore, the formation of water films on the leaf surface of plants inside the chamber could be continuously tracked with our custom built leaf wetness sensors. Using this modified leaf wetness sensor measuring the electrical surface conductance on the leaves, an exponential relationship between the ambient humidity and the electrical surface conductance could be determined.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".