Carbon storage in a chronosequence of red spruce (<i>Picea rubens</i>) forests in central Nova Scotia, Canada
Bibliographic record
Abstract
Red spruce ( Picea rubens Sarg.) forests are an ecologically and economically important forest type in eastern Canada. We quantified the carbon (C) stocks of natural red spruce dominated stands in central Nova Scotia. Twenty-four stands over a 140 year chronosequence were sampled. Within each stand, major C pools including above- and below-ground tree biomass, shrub and herb vegetation, dead organic matter, and upper (0–10 cm) mineral soil were measured. A nonlinear four-parameter logistic function was fitted to the total site C stock data to describe the change in total ecosystem C storage over time. Total site C storage increased throughout stand development in a general sigmoidal pattern, increasing from 94.4 Mg C·ha–1in the youngest age-class to a maximum of 247.0 Mg C·ha–1in the 81- to 100-year-old age-class, then decreasing in the oldest age-classes. Carbon pools of live vegetation, standing dead trees, and downed woody debris displayed recognizable changes in C storage throughout stand development, conforming to some of the fundamental ideas on forest stand dynamics. Overall, above- and below-ground tree biomass had the greatest influence on total site C storage dynamics. These results are likely to be integrated into further forest management plans and generalized in other contexts to evaluate carbon stocks at the regional scale.
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.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".