Amino acid accumulation and growth of<i>Sphagnum</i>under different levels of N deposition
Bibliographic record
Abstract
Nitrogen (N) is a critical nutrient for Sphagnum mosses dominating mire ecosystems. We simulated N deposition by adding doses of NH4NO3 (0, 1, 3, 5 and 10 g m−2 yr−1) to two Swedish mires with different levels of background atmospheric N deposition, i.e., on Luttumyren in central Sweden 0.3-0.4 g N m−2 yr−1 and 0.7-1.1 g N m−2 yr−1 on Åkhultmyren in south Sweden. After two years of NH4NO3 additions, free amino acid concentrations of S. fuscum, S. magellanicum and S. rubellum from the two mires were analyzed and length growth of the mosses were measured. N additions increased amino acid concentrations in Sphagnum capitula, whereas it decreased Sphagnum length growth. In general, we found that when Sphagnum amino acid N concentrations exceeded 2.0 mg amino acid N g−1 dry mass, Sphagnum length growth was reduced. The decreased growth did not explain the variation in amino acid concentrations. Hence, increased Sphagnum N assimilation in N treated plots was most likely the factor causing tissue amino acid concentrations to increase. Significant differences among control plots between the two mires in Sphagnum total amino acid N concentrations did not occur. Total amino acid N concentrations of Sphagnum are thus not sensitive enough to reflect differences in N deposition rates when they are below 1.0 g m−2 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 | 0.000 | 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".