Inter- and intra-varietal variation in aerobic methane emissions from environmentally stressed pea plants
Bibliographic record
Abstract
Environmental stress factors can influence methane (CH4) emissions from plants. There are a few studies on the interactive effects of stress factors on plant aerobic CH4, but none on the comparative evaluation of CH4 emissions between and among plant varieties. We examined the effects of temperature, UVB radiation, and watering regime on CH4 emissions from 10 pea (Pisum sativum L.) varieties first and then selected two varieties with the highest (237J Sundance; var. 1) and lowest (422 Ho Lan Dow; var. 2) emissions for further studies. Plants were grown in controlled-environment growth chambers under two temperature regimes (22 °C / 18 °C and 28 °C / 24 °C, 16 h light / 8 h dark), two UVB levels (0 and 5 kJ·m−2·d−1), and two watering regimes (well-watered and water-stressed) for 14 days, after one week of growth under 22 °C / 18 °C. Higher temperatures and water stress increased CH4 emissions, and increased emission was associated with stress. Pea varieties varied in growth and CH4 emissions; var. 1 was more stressed and had higher emission than var. 2. In the stressed variety, the water-stressed plants grown under higher temperatures at UVB5 had the highest CH4 emission, whereas the well-watered plants grown under lower temperatures at UVB5 had the lowest emission. We conclude that climatic stress conditions increase CH4 emissions, which vary with plant varieties.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".