Light and Hydrogen Sulfide Cause Multilevel Disruption of Carbon Metabolism in the Seagrass <i>Halophila ovalis</i>
Bibliographic record
Abstract
Seagrasses are critical global carbon sinks declining at a rapid pace. Phytotoxic hydrogen sulfides (H 2 S) and light deprivation are known drivers of seagrass loss worldwide; however, the underlying physiological mechanisms are not well understood. To address this knowledge gap, we explored the fate of inorganic carbon (C i ) in Halophila ovalis which were exposed to either low light (88% shade), (ii) sediment H 2 S stress, or (iii) both stressors combined in a mesocosm setting. Using a novel multidisciplinary approach in combination with a 13 C tracer (NaH 13 CO 3 ), we investigated differences in C i acquisition, metabolite incorporation, and carbon translocation. C i acquisition into seagrass leaves was impacted by both H 2 S and low light stress, synergistically reducing carbon acquisition rates by 10.9-fold. The incorporation of 13 C into leaf sugar pools was also affected by both stressors. In addition, low light impacted critical intermediates of both glycolysis and the tricarboxylic acid cycle. Below-ground data suggest that H 2 S interferes with carbon translocation from the leaf into the rhizome and caused an 85% reduction in rhizome growth, irrespective of light. Overall, this study suggests that it is likely a multilevel (acquisition, metabolism, translocation) disruption of the carbon budget that threatens seagrass health and survival under both H 2 S and low light stress.
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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 | 0.000 |
| 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 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".