Identification of dissolved organic matter size components in freshwater and marine environments
Bibliographic record
Abstract
Abstract Dissolved organic matter (DOM) in the transition zone from freshwater to marine systems was analyzed with a new approach for parameterizing the size distribution of organic compounds. We used size‐exclusion chromatography for molecular size analysis and quantified colored DOM (CDOM) on samples from two coastal environments in the Baltic Sea (Roskilde Fjord, Denmark and Gulf of Gdansk, Poland). We applied a Gaussian decomposition method to identify peaks from the chromatograms, providing information beyond bulk size properties. This approach complements methods where DOM is separated into size classes with pre‐defined filtering cutoffs, or methods where chromatograms are used only to infer average molecular weight. With this decomposition method, we extracted between three and five peaks from each chromatogram and clustered these into three size groups. To test the applicability of our method, we linked our decomposed peaks with salinity, a major environmental driver in the freshwater‐marine continuum. Our results show that when moving from freshwater to low‐salinity coastal waters, the observed steep decrease of apparent molecular weight is mostly due to loss of the high‐molecular‐weight fraction (HMW; >2 kDa) of CDOM. Furthermore, most of the CDOM absorbance in freshwater originates from HMW DOM, whereas the absorbing moieties are more equally distributed along the smaller size range (< 2 kDa) in marine samples.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".