Light quality and quantity regulate aerobic methane emissions from plants
Bibliographic record
Abstract
Studies have been mounting in support of the finding that plants release aerobic methane (CH4), and that these emissions are increased by both short‐term and long‐term environmental stress. It remains unknown whether or not they are affected by variation in light quantity and quality, whether emissions change over time, and whether they are influenced by physiological parameters. Light is the primary energy source of plants, and therefore an important regulator of plant growth and development. Both shade‐intolerant sunflower and shade‐tolerant chrysanthemum were investigated for the release of aerobic CH4 emissions, using either low or high light intensity, and varying light quality, including control, low or normal red:far‐red ratio (R:FR), and low or high levels of blue, to discern the relationship between light and CH4 emissions. It was found that low levels of light act as an environmental stress, facilitating CH4 release from both species. R:FR and blue lights increased emissions under low light, but the results varied with species, providing evidence that both light quantity and quality regulate CH4 emissions. Emission rates of 6.79–41.13 ng g−1 DW h−1 and 18.53–180.25 ng g−1 DW h−1 were observed for sunflower and chrysanthemum, respectively. Moreover, emissions decreased with age as plants acclimated to environmental conditions. Since effects were similar in both species, there may be a common trend among a number of shade‐tolerant and shade‐intolerant species. Light quantity and quality are influenced by factors including cloud covering, so it is important to know how plants will be affected in the context of aerobic CH4 emissions.
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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 | 0.001 |
| 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".