Cellulose, Peat, and Mixed Vegetation in situ Decomposition in a Fen Exposed to Increasing Nitrogen Treatments, 2012-2015
Bibliographic record
Abstract
Development of the oil sands has led to increasing atmospheric N deposition, with values as high as 17 kg N ha-1 yr-1; regional background levels <2 kg N ha-1 yr-1. To examine responses to N deposition, over five years, we experimentally applied N (as NH4NO3) to a fen near Mariana Lake, Alberta, at rates of 0, 5, 10, 15, 20, and 25 kg N ha-1 yr-1, plus controls (no water or N addition). We examined the effects of N addition on cellulose, peat, and mixed vegetation placed in the fen from 2012-2015 and collected after 5 and 17 months. Decomposition of cellulose filter paper increased with increasing N addition; the slopes of the responses did not differ between years, although decomposition was faster for filter paper placed in the field in 2014 than in either 2012 or 2013. In contrast, decomposition of Sphagnum moss was unaffected by N addition. Vascular plant litter decomposition decreased with N addition in 2015, but not 2014, Water addition alone had no significant effect on cellulose decomposition k values in any of the three years (p > 0.26) or on vascular plant litter mass in either year (p = 0.81). However, Sphagnum mass loss was significantly higher in the control treatment than in 0 kg N ha-1 yr-1 treatment (p = 0.0046) averaged over the two years of decomposition. Assessment of decomposition and its controls may be especially important in peatlands, as the development and persistence of peat depends on an excess of NPP over decomposition throughout the peat profile. There is evidence that increasing N deposition/availability stimulates cellulose decomposition in surface fen peat, as we found previously at Mariana Lake Bog, however, bog material decomposition appears to be more complicated.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".