Seasonality of photochemical dissolved organic carbon mineralization and its relative contribution to pelagic CO<sub>2</sub> production in northern lakes
Bibliographic record
Abstract
Abstract Boreal and northern temperate lakes (hereinafter referred to as northern lakes) are sites of intense processing of dissolved organic carbon (DOC), which is reflected in part in the persistent CO2 supersaturation of their surface waters. These ecosystems are subject to strong seasonal fluctuations in both irradiance and DOC amount and quality, which in turn should result in temporal shifts in the magnitude of DOC photodegradation. Here we explore the temporal patterns in the magnitude of water column DOC photomineralization and its potential contribution to pelagic CO2 production in three northern lakes of different DOC content. We performed laboratory DOC photodegradation incubations and combined the resulting rates with field measurements and modeling to reconstruct the annual cycle in depth‐integrated DOC photomineralization. We found that areal rates of DOC photomineralization were driven by both irradiance and intrinsic DOC photoreactivity, both of which showed seasonality. Over an annual cycle, depth‐integrated DOC photomineralization rates were remarkably similar across lakes, averaging 4.4 (SD = 0.7) g C m−2 yr−1 and daily rates followed an apparent seasonal pattern. The contribution of DOC photomineralization to total pelagic CO2 production (as the sum of respiration and DOC photomineralization) peaked after ice melt (up to 49%), averaging 14% for the entire open water season. Our study identifies potential hot periods of photochemical activity that result from the interplay between DOC properties and environmental conditions, which should be incorporated into models of lake functioning.
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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".