Predicting Dissolved Lignin Phenol Concentrations in the Coastal Ocean from Chromophoric Dissolved Organic Matter (CDOM) Absorption Coefficients
Bibliographic record
Abstract
Dissolved lignin is a well-established biomarker of terrigenous dissolved organic matter (DOM) in the ocean, and a chromophoric component of DOM. Although evidence suggests there is a strong linkage between lignin concentrations and chromophoric DOM (CDOM) absorption coefficients in coastal waters, the characteristics of this linkage and the existence of a relationship that is applicable across coastal oceans remain unclear. Here, 421 paired measurements of dissolved lignin concentrations (sum of 9 lignin phenols) and CDOM absorption coefficients (ag(λ)) were used to examine their relationship along the river-ocean continuum (0-37 salinity) and across contrasting coastal oceans (sub-tropical, temperate, high-latitude). Overall, lignin concentrations spanned four orders of magnitude and revealed a strong, non-linear relationship with ag(λ). The characteristics of the relationship (shape, wavelength dependency, lignin-composition dependency) and evidence from degradation indicators were all consistent with lignin being an important driver of CDOM variability in coastal oceans, and suggested physical mixing and long-term photodegradation were important in shaping the relationship. These observations were used to develop two simple empirical models for estimating lignin concentrations from ag(λ) with a +/- 20% error relative to measured values. The models are expected to be applicable in most coastal oceans influenced by terrigenous inputs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".