Evaluating the Performance of a Forest Succession Model to Predict the Long-Term Dynamics of Tree Species in Mixed Boreal Forests Using Historical Data in Northern Ontario, Canada
Bibliographic record
Abstract
Environmental concerns and economic pressures on forest ecosystems have led to the development of sustainable forest management practices. As a consequence, forest managers must evaluate the long-term effects of their management decisions on potential forest successional pathways. As changes in forest ecosystems occur very slowly, simulation models are logical and efficient tools to predict the patterns of forest growth and succession. However, as models are an imperfect representation of reality, it is desirable to evaluate them with historical long-term forest data. Using remeasured tree and stand data from three data sets from two ecoregions in northern Ontario, the succession gap model ZELIG-CFS was evaluated for mixed boreal forests composed of black spruce (Picea mariana [Mill.] B.S.P.), balsam fir (Abies balsamea [L.] Mill.), jack pine (Pinus banksiana L.), white spruce (Picea glauca [Moench] Voss), trembling aspen (Populus tremuloides Michx.), white birch (Betula papyrifera Marsh.), northern white cedar (Thuja occidentalis L.), American larch (Larix laricina [Du Roi] K. Koch), and balsam poplar (Populus balsamefera L.). The comparison of observed and predicted basal areas and stand densities indicated that ZELIG-CFS predicted the dynamics of most species consistently for periods varying between 5 and 57 simulation years. The patterns of forest succession observed in this study support gap phase dynamics at the plot scale and shade-tolerance complementarity hypotheses at the regional scale.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".