The sensitivity of nitrogen fixation by a feathermoss–cyanobacteria association to litter and moisture variability in young and old boreal forests
Bibliographic record
Abstract
We conducted a pair of experiments to assess whether nitrogen (N) fixation by a feathermoss–cyanobacteria association was sensitive to moisture availability and quality of litter inputs, and whether sensitivity to these factors differed between young and old forests. In our first greenhouse experiment, we experimentally varied the frequency of water addition to Pleurozium schreberi (Brid.) Mitt. collected from young and old forest sites. This experiment revealed that the extreme drought treatment reduced N fixation capacity (measured via acetylene reduction), whereas daily watering increased N fixation capacity. The experiment also demonstrated that sensitivity to moisture variability was greater in old forests than in young forests. In a second greenhouse experiment, we repeatedly applied litter extracts from six common boreal species, Pinus sylvestris L., Picea abies (L.) Karst., Betula pubescens Ehrh., Vaccinium myrtillus L., Vaccinium vitis-idaea L., and Empetrum hermaphroditum Lange ex Hagerup. After 43 days, we found no significant effects of litter or litter by stand age interaction on N fixation capacity of P. schreberi, whereas stand age remained a significant factor. These experiments suggest that the N fixation capacity of the P. schreberi – cyanobacteria association is relatively resistant to short-term variation of litter as an environmental driver but that precipitation extremes in old forests may significantly alter the N fixation capacity of the association.
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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 | 0.000 | 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 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".