Calibration and validation of a simplified process-based model for the prediction of the carbon balance of Scottish Sitka spruce (<i>Picea sitchensis</i>) plantations
Bibliographic record
Abstract
There is increasing recognition that forestry provides a low cost and robust means of climate change abatement through carbon sequestration and substitution. However, current understanding of forest ecosystem carbon exchange and forest–atmosphere interactions are often inadequately characterized by existing empirical growth models with resulting poor representation for regional extrapolations. In this paper, we describe the parameterisation and independent validation, against both eddy covariance and forest growth experimental data, of a process-oriented model 3PGN to provide assessments of carbon sequestration of Sitka spruce (Picea sitchensis (Bong.) Carrière) plantations across Scotland. In comparison with eddy covariance measurements, the model predicted all of the major annual carbon fluxes, i.e., gross primary production (PG), net ecosystem production (PE), and ecosystem respiration (RE), with biases lower than 10%. At a monthly time step, only PG and PE were accurately estimated, whereas RE was not. At longer time scales (i.e., several decades), the model reliably represented the major patterns of the carbon balance. Soil type was identified as the important factor influencing site productivity; fertilization practices did not alter long-term site nutritional status. The analyses also highlighted the potential impact of carbon loss from carbon-rich soils, which can result in differences between optimal rotation length for carbon sequestration and for timber production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".