Effects of stand age on net primary productivity of boreal black spruce forests in Ontario, Canada
Bibliographic record
Abstract
Quantification of the effects of stand age on its net primary productivity (NPP) is critical for estimating forest NPP and carbon budget at regional to global scales. This paper reports a practical method for quantifying ageNPP relationships using existing normal yield tables, biomass equations, and measurements of fine-root turnover and litterfall. Applying this method, we developed mean ageNPP relationships for black spruce (Picea mariana (Mill.) BSP) stands in Ontario. We define "mean ageNPP relationship", as the changes in NPP that occur with age under long-term mean environmental conditions. These relationships indicate that NPP at more productive sites culminates to a higher value and at an earlier age and also declines more rapidly thereafter. A further component analysis indicates that the decrease in biomass growth of woody tissues is the main contributor to the decline with age. Finally, error assessment suggests that the uncertainty in NPP estimates can be substantially reduced with a better quantification of fine-root turnover and litterfall, which are the two dominant NPP components, particularly in the later stages of stand development. With new techniques now available, more accurate measurement of these components is possible, and thus strongly recommended.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".