Temporal Decoupling between Total Organic Carbon and Iron in Lakes Linked to Interannual Changes in Precipitation
Bibliographic record
Abstract
Widespread increases in lake browning, which affects primary production, have been observed in northern lakes. While lake browning is attributed to increases in terrestrially derived total organic carbon (TOC) and total iron (Fe), Fe does not consistently correlate with increasing TOC over time. This temporal mismatch between TOC and Fe indicates that we still do not fully understand the causes of lake browning, especially in the context of gradually changing climatic conditions. In this study, we utilized Fennoscandian (Swedish and Finnish) 30-year (1990-2020) time series data for 102 lakes to describe possible reasons for the temporal decoupling between TOC and Fe. Using wavelet coherence analysis, we found evidence of interannual response of median TOC and Fe concentrations to changes in precipitation patterns. Although TOC and Fe were increasing in most lakes following an increase in precipitation (indicating they were temporally coupled), 41% of the lakes—typically larger with shorter water residence times—exhibited a declining trend in Fe. This decline was associated with short-term (2 to 4 years) increases in precipitation, leading to temporal decoupling. Furthermore, we found no evidence that rising air temperatures or declining sulfur (S) deposition influence the coupling or decoupling patterns between TOC and Fe. Our findings suggest that natural variability of lake types and their responses to changing climatic conditions (here primarily changes in precipitation) are the main factors contributing to temporal decoupling of TOC and Fe in some Fennoscandian lakes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".