Evaluation of a mechanistic approach to mixedwood modelling
Bibliographic record
Abstract
Mixed conifer–broadleaf forests (mixedwoods), covering more than a third of the productive forest landbase in BC, are highly valuable both as sources of fibre and as areas rich in biodiversity. In recognition of the multiple benefits of this forest type, management paradigms have transitioned from a focus on promoting conifer plantations in mixedwood areas to the management of intimate mixtures. The exceptionally dynamic growth properties and species interactions in mixedwood forests present a challenge for projecting the growth and development of different types of mixedwoods and their response to different silviculture systems. Here we evaluate the ability of a mechanistic forest growth model (FORECAST) to project patterns of stand growth and dynamics in two mixedwood forest types subjected to different silvicultural treatments. Model output is compared against field measurements from long-term silviculture trials in the Sub Boreal Spruce (SBS)—18 years, and Interior Cedar Hemlock (ICH)—10 years, biogeoclimatic zones in British Columbia, Canada. FORECAST was able to reproduce patterns of growth response in both mixedwood forest types with reasonable accuracy. An analysis of the simulated relative impact of light and nutrient competition on growth dynamics and treatment response is provided. Results suggest that competition for both light and nutrients are important factors in the dynamics of these mixedwood forest types and that long-term response data and modelling are required to adequately assess the rotation-length effects of treatments on stand development. The analysis described herein provides a level of confidence for the use of the model as a decision-support tool in these ecosystem types, but more validation work should be conducted across a range of different mixedwood forest types and management interventions as long-term datasets become available. Key words: FORECAST, mixedwood management, model testing, process-based model, resource competition
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".