Lipoxygenase-induced autoxidative degradation of terrestrial particulate organic matter in estuaries: A widespread process enhanced at high and low latitude
Bibliographic record
Abstract
There exists a substantial amount of research on abiotic (e.g. photochemical) degradation pertaining to organic matter (OM) in the marine realm. While recent research has shown its importance in the degradation of terrestrial particulate OM (TPOM), the mechanisms involved in the induction of autoxidation in estuaries remain unclear. In this study, we propose for the first time the involvement of lipoxygenase (LOX) activity in the induction of autoxidation in mixed waters. The observation of unusual profiles of palmitoleic acid oxidation products and the presence of jasmonic acid in suspended particulate matter (SPM) collected close to the Rhône River, as well as in samples from the Mackenzie and Amazon rivers, is attributed to strong LOX activity. We show the role played by salinity in the induction of this LOX activity and provide an explanation for the differences in estuarine autoxidation level. At high latitude, lower temperatures and irradiance favor photooxidative damage to higher plant debris and, consequently, hydroperoxide production. High hydroperoxide content strongly contributes to LOX activation in mixed waters. The high resulting LOX activity enhances alkoxyl radical production and thus autoxidation. On the contrary, at low latitude, photooxidative effects are limited, and riverine autoxidation is favored. The higher hydroperoxide content of TPOM may, as a consequence, thereby also contribute to a high level of LOX activity and autoxidation in estuaries. In temperate zones , land and riverine photooxidative and autoxidative damage is limited, unlike estuaries where we observed significant LOX-induced and autoxidative damage.
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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) | 0.000 | 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".