Benthic microalgae offset the sediment carbon dioxide emission in subtropical mangrove in cold seasons
Bibliographic record
Abstract
Abstract Our study examined the relationship of microphytobenthos to greenhouse gas fluxes from sediments of a subtropical mangrove forest and adjacent mudflat in the Jiulong River Estuary, South China. The relationship between chlorophyll a concentration at the sediment surface and diatom density confirmed that these microalgae were the important component of the microphytobenthos, which produced an observable biofilm in cold seasons (winter and spring) on both the mangrove and mudflat sediment surfaces. Fluxes of methane and nitrous oxide were not affected by the microalgae film and were similar between the mangrove and mudflat. However, benthic microalgae affected the sediment to atmosphere carbon dioxide (CO2) flux, and the effect was temporally variable with the seasonal change in microalgae abundance. In the cold seasons, the mangrove sediment was a CO2 sink under light chambers but a source under dark chambers. In summer, when there was no visible microalgae film at the sediment surface, the intertidal sediments had CO2 emissions and comparable fluxes between the two chambers. The negative daily CO2 fluxes of the film‐covered sediment (as the average of the dark and light fluxes) and positive flux of the sediment without visible biofilm indicated that the occurrence of microalgae film converted the mangrove sediment from a CO2 source to a sink and that the photosynthesis of the microalgae film offset the sediment respiration during the cold seasons in this study. We also found similar effects of microalgae on CO2 fluxes on the nonvegetated mudflat.
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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) | 0.000 | 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 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".