Long‐term development and trajectories of inferred lake‐water organic carbon and <scp>pH</scp> in naturally acidic boreal lakes
Bibliographic record
Abstract
Abstract Monitoring of surface waters in the boreal region over the last decades shows that waters are becoming browner. This timeframe may not, however, be sufficient to capture underlying trajectories and driving mechanisms of lake‐water quality, important for prediction of future trajectories. Here we synthesize data from seven lakes in the Swedish boreal landscape, with contemporary lake‐water total organic carbon (TOC) concentrations of 1.4–14.4 mg L−1, to conceptualize how natural and particularly human‐driven processes at the landscape scale have regulated lake‐water TOC levels over the Holocene. Sediment‐inferred trends in TOC are supported by several proxies, including diatom‐inferred pH. Before ~ 700 ce, all lakes were naturally acidic (pH 4.7–5.4) and the concentrations of inferred lake‐water TOC were high (10–23 mg L−1). The introduction of traditional human land use from ~ 700 ce led to a decrease in lake‐water TOC in all lakes (to 5–14 mg L−1), and in four poorly buffered lakes, also to an increase in pH by > 1 unit. During the 20th century, industrial acid deposition was superimposed on centuries of land use, which resulted in unprecedentedly low lake‐water TOC in all lakes (3–11 mg L−1) and severely reduced pH in the four poorly buffered lakes. The other lakes resisted pH changes, likely due to close connections to peatlands. Our results indicate that an important part of the recent browning of boreal lakes is a recovery from human impacts. Furthermore, on a conceptual level we stress that contemporary environmental changes occur within the context of past, long‐term disturbances.
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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 | 0.001 | 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".