Application of the forest ecosystem model EFIMOD 2 to jack pine along the Boreal Forest Transect Case Study
Bibliographic record
Abstract
Sustainability of forest ecosystems and climate change are two critical issues for boreal forest ecosystems in Canada that require an understanding of the links and balance between productivity, soil processes and their interaction with natural and anth ropogenic disturbances. Forest ecosystem models can be used to understand and predict boreal forest ecosystem dynamics. EFIMOD 2 is an individual tree model of the forest-soil ecosystem capable of modelling nitrogen feedback to productivity in response to changes in soil moisture and temperature. It has been successfully applied in Europe, but has not been calibrated for any forest ecosystem in Canada. The objective of this study was to parameterize and validate EFIMOD 2 for jack pine in Canada. Simulated and measured results agreed for changes in tree biomass carbon and soil carbon and nitrogen with increasing stand age and across a climatic gradient from the southern to northern limits of the boreal forest. Preliminary results from scenario testing indicate that EFIMOD 2 can be successfully applied to predict the impacts of forest management practices and climate change in the absence of natural disturbances on jack pine in the boreal forest of Canada. Model development is underway to represent the effects of natural disturbances. Key words: EFIMOD 2, forest soil, carbon, nitrogen, model, jack pine
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".