A multi-species, process based vegetation simulation module to simulate successional forest regrowth after forest disturbance in daily time step hydrological transport models
Bibliographic record
Abstract
To simulate the effects of tree harvest on boreal forest catchment hydrology, the vegetation growth model within the soil and water assessment tool (SWAT) must reproduce the successional stages of forest reestablishment. The agricultural land management alternatives with numerical assessment criteria (ALMANAC), a multi-species growth model, was modified to simulate vegetation regeneration on forest sites after harvest (ALMANAC BF ). The model uses similar principles of vegetation growth as the current vegetation model in SWAT, and input requirements are consistent with typical forest inventory databases. This article describes the algorithms integrated into the ALMANAC BF model to simulate successional stages in forest growth, provides initial estimates of parameters required to simulate multi-species forest succession, and presents examples of the type of variability in vegetation growth scenarios that these algorithms can reproduce. The model structure and modelling approach shows promise as a tool for foresters to evaluate how patterns and timing of forest management activities influence forest watershed hydrology.
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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 | 0.000 | 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 | 0.001 |
| 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".