Biological and photochemical reactivity of dissolved organic matter in a large temperate river
Bibliographic record
Abstract
Abstract Large rivers are critical conduits from continents to oceans as they receive, produce and process huge amounts of dissolved organic matter (DOM). Yet, the relative influence of intrinsic DOM properties and extrinsic environmental properties on these processes at the ecosystem‐level is rarely studied. We assessed DOM optical properties as well as bioreactivity and photoreactivity at 40 sites along a >200 km transect of the freshwater portion of the St. Lawrence River through a series of standardized microbial incubations and exposure to simulated sunlight, and then estimated in situ areal rates of processing. We found that biological and photochemical processes preferentially targeted contrasting pools of DOM, but that DOM composition had an undiscernible effect on in situ degradation rates compared to other environmental factors. Total daily processing across the whole water column ranged from 36.7 to 892.1 mg C m−2. In situ photochemical degradation was largely driven by intrinsic DOM photoreactivity rather than environmental drivers in the water. In contrast, we found a relatively constant baseline pool of biolabile DOM that appeared to be independent from changes in concentration and environmental conditions. In situ DOM processing was mostly driven by biological degradation (on average 95%), and disproportionately high biodegradation rates (2.5–4x the average) were found in a few shallower sites near effluents or islands, potentially driven by local increases in nutrient concentration and in the proportion of protein‐like DOM. These results illustrate how DOM composition and degradability interact with ambient environmental and morphological properties to dictate an ecosystem‐level reactivity of DOM.
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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 | 0.001 | 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".