Experimental nitrogen addition alters structure and function of a boreal poor fen: Implications for critical loads
Bibliographic record
Abstract
Bogs and fens cover 6 and 21%, respectively, of the 140,329 km2 Oil Sands Administrative Area in northern Alberta. Regional background atmospheric N deposition is low (<2 kg N ha−1 yr−1), but oil sands development has led to increasing N deposition (as high as 17 kg N ha−1 yr−1). To examine responses to N deposition, over five years, we experimentally applied N (as NH4NO3) to a poor fen near Mariana Lake, Alberta, unaffected by oil sands activities, at rates of 0, 5, 10, 15, 20, and 25 kg N ha−1 yr−1, plus controls (no water or N addition). At Mariana Lake Poor Fen (MLPF), increasing N addition: 1) progressively inhibited N2-fixation; 2) had no effect on net primary production (NPP) of Sphagnum fuscum or S. angustifolium, while stimulating S. magellanicum NPP; 3) led to decreased abundance of S. fuscum and increased abundance of S. angustifolium, S. magellanicum, Andromeda polifolia, Vaccinium oxycoccos, and of vascular plants in general; 4) led to an increase in stem N concentrations in S. angustifolium and S. magellanicum, and an increase in leaf N concentrations in Chamaedaphne calyculata, Andromeda polifolia, and Vaccinium oxycoccos; 5) stimulated root biomass and production; 6) stimulated decomposition of cellulose, but not of Sphagnum or vascular plant litter; and 7) had no or minimal effects on net N mineralization in surface peat, NH4+-N, NO3−-N or DON concentrations in surface porewater, or peat microbial composition. Increasing N addition led to a switch from new N inputs being taken up primarily by Sphagnum to being taken up primarily by shrubs. MLPF responses to increasing N addition did not exhibit threshold triggers, but rather began as soon as N additions increased. Considering all responses to N addition, we recommend a critical load for poor fens in Alberta of 3 kg N ha−1 yr−1.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".