Distribution and microbial metabolism of dimethylsulfoniopropionate and dimethylsulfide during the 2007 Arctic ice minimum
Bibliographic record
Abstract
[1] The distribution and biological cycling of the climate active trace gas dimethylsulfide (DMS) and its algal precursor dimethylsulfoniopropionate (DMSP) were characterized at 20 stations across the Canadian High Arctic during fall 2007. Transformation rates of DMSP and production rates of DMS from dissolved DMSP (DMSPd) were measured during 3 h onboard incubations with radioactively labeled 35S-DMSP. Particulate DMSP (DMSPp) in surface waters varied between 2 and 39 nmol L−1 and increased with chlorophyll a (Chl a) concentrations (r = 0.84). DMS concentrations in surface waters ranged from 0.05 to 0.8 nmol L−1 and were positively correlated with DMSPp (r = 0.89) and Chl a (r = 0.74). The DMSPd loss rate constant varied from 0.01 to 0.14 h−1 and was also positively correlated with Chl a concentrations (r = 0.67). The turnover time of the DMSPd pool varied between 0.3 and 3.4 days (mean = 0.96 day). Bacterial DMS production varied between 0.01 and 0.51 nmol L−1 d−1 (mean = 0.14 nmol L−1 d−1). Assuming local steady state conditions at the time scale of a day, the turnover time of the DMS pool based only on production from DMSPd was ∼6 days at the sampling stations. This long turnover time suggests that DMS production was dominated by nonbacterial processes during our study. Our results show that DMS production could persist at low rates in late fall under ice-free conditions. The magnitude of this production appears to be limited by the low algal and bacterial production prevailing at that time.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".