Amino acid accumulation and growth of<i>Sphagnum</i>under different levels of N deposition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it