Impact of anthropogenic pH perturbation on dimethyl sulfide cycling
Bibliographic record
Abstract
The objective of this study was to assess experimentally the potential impact of anthropogenic pH perturbation (ApHP) on concentrations of dimethyl sulfide (DMS) and dimethylsulfoniopropionate (DMSP), as well as processes governing the microbial cycling of sulfur compounds. A summer planktonic community from surface waters of the Lower St. Lawrence Estuary was monitored in microcosms over 12 days under three pCO2 targets: 1 × pCO2 (775 µatm), 2 × pCO2 (1,850 µatm), and 3 × pCO2 (2,700 µatm). A mixed phytoplankton bloom comprised of diatoms and unidentified flagellates developed over the course of the experiment. The magnitude and timing of biomass buildup, measured by chlorophyll a concentration, changed in the 3 × pCO2 treatment, reaching about half the peak chlorophyll a concentration measured in the 1 × pCO2 treatment, with a 2-day lag. Doubling and tripling the pCO2 resulted in a 15% and 40% decline in average concentrations of DMS compared to the control. Results from 35S-DMSPd uptake assays indicated that neither concentrations nor microbial scavenging efficiency of dissolved DMSP was affected by increased pCO2. However, our results show a reduction of the mean microbial yield of DMS by 34% and 61% in the 2 × pCO2 and 3 × pCO2 treatments, respectively. DMS concentrations correlated positively with microbial yields of DMS (Spearman’s ρ = 0.65; P < 0.001), suggesting that the impact of ApHP on concentrations of DMS in diatom-dominated systems may be strongly linked with alterations of the microbial breakdown of dissolved DMSP. Findings from this study provide further empirical evidence of the sensitivity of the microbial DMSP switch under ApHP. Because even small modifications in microbial regulatory mechanisms of DMSP can elicit changes in atmospheric chemistry via dampened efflux of DMS, results from this study may contribute to a better comprehension of Earth’s future climate.
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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".