Seasonal patterns of climate controls over nitrogen fixation by <i>Alnus viridis</i> subsp. <i>fruticosa</i> in a secondary successional chronosequence in interior Alaska
Bibliographic record
Abstract
Patterns of and controls over N2 fixation by green alder were studied in post-fire, mid-succession, and white spruce upland forests in interior Alaska, focusing on the hypothesis that ecosystem-level nitrogen (N) inputs decrease with successional development. N2-fixation rates tracked plant phenology during the 1997 (drought) and 1998 (normal precipitation) growing seasons. The best model for predicting acetylene reductase activity (ARA, μmol C2H4·g noduleDWT−1·h−1) across all stands and sampling periods included a linear response to soil temperature and a quadratic response to Julian day (r2 = 0.23, P < 0.0001). There were few significant relationships between seasonal maximum values for ARA and measured leaf traits; however, we did detect an inverse correlation between foliar N to P ratio and seasonal maximum ARA. During 1998, stands with higher maximum ARA had higher soil %N, and maximum ARA was positively correlated with subcanopy %P in O and A soil horizons. During 1997, leaf %P and N resorption were lower and leaves were thinner compared to 1998. Drought effects were most pronounced in mid-succession, where alder exhibited reduced ARA, leaf %P, leaf thickness, and lower leaf resorption of P and N. Although ARA and nodule biomass did not differ among stand types, increases in alder densities with successional time translated to increasing ecosystem-level N inputs across the chronosequence. These results contradict established theory predicting a decline in N2-fixation rates and N2-fixer abundance during successional stand development.
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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 | 0.001 | 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".