Coherent responses of sulphate concentration in Norwegian lakes: relationships with sulphur deposition and climate indices
Bibliographic record
Abstract
Abstract. The coherence or synchrony in the trends in SO42– concentration in a set of 100 lakes in Norway that have a long-term chemical record was evaluated. Using a statistical technique that compares patterns or trends that are not uni-directional, the lakes were grouped into 18 subsets or clusters, each with between 2 and 11 lakes that had similar trends. These temporal trends were strongly correlated with several climate indices, notably the Arctic Oscillation Index (AOI) measured in the autumn, and the annual North Atlantic Oscillation Index (NAOI). Because these clusters of lakes were spatially dispersed, they could not be compared directly with trends in wet S deposition, because S deposition varied substantially between lakes within each cluster. However, the average trend in SO42– concentration was evaluated in each of 10 regions of Norway that were defined previously on the basis of pollution load, meteorological variables and biogeography. Although these regions did not match the statistically-selected clusters of lakes with equal trends very closely, there were similar, strong correlations between climate indices (the AOI and NAOI) and the 10 average SO42– trends, although there were even stronger relationships with average wet S deposition in the regions. When subsets of lakes with coherent SO42– trends were selected from within each of the 10 regions, both wet S deposition and the climate indices were strongly correlated with those SO42– trends. Hence, lakes in Norway respond to changes in wet S deposition and are influenced by large-scale, i.e. global, climate signals. Future evaluation of recovery of lakes affected by acid deposition must therefore consider the confounding effects of climate and potential climate change. Keywords: recovery, acid deposition, coherence, sulphate, climate change
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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 | 0.001 | 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 | 0.001 |
| 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".