Fine root dynamics with stand development in the boreal forest
Bibliographic record
Abstract
Summary 1. Despite greater importance of below ground in influencing terrestrial carbon and nutrient cycling than above ground, how below‐ground biomass, production, turnover and mortality change with stand development remains poorly understood. 2. Here, we used a postfire boreal forest chronosequence that spanned over 200 years (3, 11, 29, 94, 142 and 205 years since fire) to study how the dynamics of fine roots (≤2 mm in diameter) vary with stand age. We collected 756 sequential cores and 270 ingrowth cores, each separated to layers and live/dead, resulting in a total of 5076 fine root samples to quantify fine root biomass, production, mortality and turnover rates. 3. With stand development, fine root biomass increased from 3 to 94‐year‐old, and declined thereafter, whereas necromass increased to 142‐year‐old and levelled off at 205‐year‐old. Fine root production and turnover increased from 3 to 11‐year‐old stands, and declined thereafter. Fine root mortality increased from 3 to 142‐year‐old stands. 4. Our study, the first to show four simultaneous stand age–dependent below‐ground attributes, indicated that fine roots in young stands turn over faster than in old stands based on the estimates from both sequential and ingrowth cores. Despite some similarities among the studied attributes, the peaks they reached were not the same. Our results suggest that fine root dynamics are influenced by changes in species composition and soil properties associated with stand development in the boreal forest, especially the increase in forest floor nitrogen: phosphorus ratios as stand age increases.
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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) | 0.001 | 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".