Sulphur deposition causes a large‐scale growth decline in boreal forests in Eurasia
Bibliographic record
Abstract
Human activity has altered climate, atmospheric carbon dioxide concentrations, and the concentrations of several pollutants over the last few decades. At the same time, short‐term reactions of tree growth to climatic variations have changed during the last few decades, for reasons that are poorly understood. However, the effects of the pollutants on growth of boreal forests in these remote areas have not been quantified, but even small changes in the productivity of boreal forest should have a large effect on the carbon balance. Systematic growth changes of Scots pine, the most important forest species in boreal Eurasia, were analyzed over the last few decades using all 40 available tree ring chronologies north of 60°N latitude from the International Tree‐Ring Data Bank. We demonstrated a long‐term growth decline of Scots pine of about 17% or 0.0025 mm per year from the 1930s to the 1980s in northern Eurasia using a mixed‐effect model. This growth decline was estimated from radial growth when the age and climate effects were factored out. Although the study sites were previously considered low‐pollution pristine environments, the growth decrease was significantly related to sulphur depositions, while nitrogen depositions appeared to increase growth. Additionally, sulphur depositions caused Scots pine forests to be more sensitive to drought. Although the negative effects of local pollution on plant growth have been widely observed, the long‐term effects of sulphur emissions and its spread to ecosystems distant from the sources of pollution have never been previously documented at such a large scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".