Traces of sunlight in the organic matter biogeochemistry of two shallow subarctic lakes
Bibliographic record
Abstract
Abstract Global environmental change alters the production, terrestrial export, and photodegradation of organic carbon in northern lakes. Sedimentary biogeochemical records can provide a unique means to understand the nature of these changes over long time scales, where observational data fall short. We deployed in situ experiments on two shallow subarctic lakes with contrasting light regimes; a clear tundra lake and a dark woodland lake, to first investigate the photochemical transformation of carbon and nitrogen elemental (C/N ratio) and isotope (δ13C, δ15N) composition in lake water particulate organic matter (POM) for downcore inferences. We then explored elemental, isotopic, and spectral (inferred lake water total organic carbon [TOC] and sediment chlorophyll a [CHLa]) fingerprints in the lake sediments to trace changes in aquatic production, terrestrial inputs and photodegradation before and after profound human impacts on the global carbon cycle prompted by industrialization. POM pool in both lakes displayed tentative evidence of UV photoreactivity, reflected as increasing δ13C and decreasing C/N values. Through time, the tundra lake sediments traced subtle shifts in primary production, while the woodland lake carried signals of changing terrestrial contributions, indicating shifts in terrestrial carbon export but possibly also photodegradation rates. Under global human impact, both lakes irrespective of their distinct carbon regimes displayed evidence of increased productivity but no conspicuous signs of increased terrestrial influence. Overall, sediment biogeochemistry can integrate a wealth of information on carbon regulation in northern lakes, while our results also point to the importance of considering the entire spectrum of photobiogeochemical fingerprints in sedimentary studies.
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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 | 0.001 | 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".