A high‐resolution survey of DMS, CO<sub>2</sub>, and O<sub>2</sub>/Ar distributions in productive coastal waters
Bibliographic record
Abstract
We present continuous, high‐resolution measurements of surface dimethylsulfide (DMS), pCO2, and O2/Ar obtained in coastal waters off British Columbia, Canada, using membrane inlet mass spectrometry (MIMS). Sampled underway at a frequency of twice per minute (every ∼160 m at 10 knots cruising speed), our data reveal fine‐scale structure in gas variability and its covariance with a number of hydrographic parameters. All parameters exhibited large ranges (pCO2, 200–747 ppm; DMS, <1–29 nM; chl a, <0.1–33 μg L−1), highlighting the dynamic nature of the study area. A strong anticorrelation between pCO2 and O2/Ar was observed across the survey region, with the distributions of these gases influenced by biology and its interplay with physical processes. In contrast, DMS levels, which varied dramatically over short distances, showed no significant correlations with any single variable for the full, high‐resolution data set. However, when measurements were binned to a much coarser spatial resolution, we found a linear relationship between surface DMS and the chlorophyll/mixed layer depth ratio. The slope of this relationship differed significantly from that previously derived from open ocean data. We used several statistical techniques to estimate the spatial variability of gases and hydrographic parameters and the inherent sampling errors associated with low‐frequency sampling approaches. These analyses emphasize the importance of high‐resolution sampling in coastal areas, particularly for DMS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.001 | 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".