The effect of stumpage prices on large-area forest growth forecasts based on socio-ecological models
Bibliographic record
Abstract
Forest ecosystems are typical examples of socio-ecological systems. However, in terms of modelling, the social aspect has been given far less attention than the ecological aspect. In this study, we modelled the impact of economic and social factors on the occurrence of harvesting. This harvest model was then integrated into an individual-based model of forest growth designed for large-area forecasts. The resulting socio-ecological model was then used to produce volume predictions for two regions of France. Among the economic factors, the annual stumpage prices in interaction with the species proved to be a significant predictor of harvest occurrence. Simulating different stumpage price evolutions made it possible to predict supply curves for the two regions. Projections until 2060 showed that increases in stumpage prices will be detrimental to standing volumes in both regions. Integrating the demand for wood products into such socio-ecological models in forestry would be a major improvement.
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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 | 0.004 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 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".