Traces of sunlight in carbon biochemistry of shallow subarctic lakes
Bibliographic record
Abstract
Sunlight fuels the drawdown and evasion of carbon in shallow northern lakes. Amplified polar warming is altering the sunlit transport and transformation of aquatic carbon at an alarming rate entailing potential for climate feedbacks. We combined experimental and retrospective approaches to explore the synoptic interlinks between underwater light, aquatic carbon biochemistry, landscape carbon cycling and climate change in two shallow subarctic lakes with divergent light and carbon regime (a clear lake low in organic carbon and a dark organic rich lake). In situ enclosures (treatments under full sunlight, sunlight without the ultraviolet [UV] spectrum, no light) were first deployed on the lakes to decipher the effect of photochemical alteration on the spectral, elemental and isotopic properties of lake water organic carbon pools under short term (four weeks) exposure. We then focused on elemental, isotopic and spectral fingerprints archived in the sediments of the lakes to trace coeval variability in aquatic primary production, terrestrial carbon transport, and underwater light under centennial climate fluctuations. We observed distinct differences in carbon biochemistry between the experimental treatments illustrating the importance of sunlight, and particularly the UV spectrum, in shaping the carbon pools of the lakes already over short time scales. Over the past centennia, sediment biogeochemical composition carried signatures of change in carbon origins (algal vs terrestrial) and shifting underwater light regime. The results shed light on how climate change and sunlight shape carbon flows in shallow northern lakes over short and long time scales.
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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) | 0.001 | 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".