Soil nitrogen accretion along a floodplain terrace chronosequence in northwest Alaska: Influence of the nitrogen-fixing shrub <i>Shepherdia canadensis</i>
Bibliographic record
Abstract
Nitrogen enters terrestrial ecosystems through multiple pathways during primary succession. We measured accumulation of total soil nitrogen and changes in inorganic nitrogen (N) pools across a 300-y sequence of river terraces in northwest Alaska and assessed the contribution of the nitrogen-fixing shrub Shepherdia canadensis. Our work compared 5 stages of floodplain succession, progressing from a sparsely vegetated silt cap to dense shrubby vegetation, balsam poplar-dominated (Populus balsamifera) and white spruce-dominated (Picea glauca) mixed forests, and old-growth white spruce forest. Total soil N (0–30 cm depth) increased throughout the age sequence, initially by 2.4 g N·m−2·y−1 during the first 120 y of terrace development, then by 1.6 g N·m−2·y−1 during the subsequent 2 centuries. Labile soil N, measured by anaerobic incubation, increased most rapidly during the first 85 y of terrace formation, then remained relatively constant during further terrace development. On recently formed terraces, Shepherdia shrubs enriched soil N pools several-fold compared to soil beneath Salix spp. shrubs or intercanopy sites. Total and labile soil N accretion was proportional to Shepherdia cover during the first century of terrace development, and mineral soil δ15N content indicated that newly formed river terraces receive substantial N through N-fixation. About half the 600 g total N·m−2 accumulated across the river terrace chronosequence occurred during the 120 y when S. canadensis was dominant. Sediment deposited by periodic flooding continued to add N to terrace soils after the decline in Shepherdia abundance and may have contributed 25% of the total N found in the floodplain terrace soils.
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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".