Anthropogenic N – A global issue examined at regional scale from soils, to fungi, roots and tree rings
Bibliographic record
Abstract
Globally increasing anthropogenic airborne emissions of reactive nitrogen (N) generate several environmental issues that require investigating how N accumulation modifies the N cycle. Tree-ring δ15N series may help understanding past and current perturbations in the forest N cycle. Although several studies have addressed this issue, most of them were of local scale or based on short δ15N series. The development of this environmental indicator however would benefit from examining, at the regional scale, the relationships of long tree-ring series with soil N biogeochemical processes. Here we explore these links for tree stands of the oil-sands region in northern Alberta, and the coal-fired power plants region in central Alberta, Canada. We characterize the tree-ring δ15N trends, the N modification rates and bacterial and fungal communities of soil samples collected in the immediate surrounding of the characterized trees. The dataset suggests that specific soil pH, and N-cycling bacterial and fungal communities influence tree-ring δ15N responses to anthropogenic emissions, correlating either directly or inversely. Overall, tree-ring δ15N series may record changes in the forest-N cycle, but their interpretation requires understanding key soil biogeochemical processes. «In nature nothing exists alone», Rachel Carson.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".