Testing the Lake States variant of FVS (Forest Vegetation Simulator) for the main forest types of northern Ontario
Bibliographic record
Abstract
The Lake States variant of the FVS (Forest Vegetation Simulator) model (LS-FVS), also known as the LS-TWIGS variant of FVS, was validated for black spruce (Picea mariana (Mill.) BSP), white spruce (Picea glauca (Moench) Voss), jack pine (Pinus banksiana Lamb.) and trembling aspen (Populus tremuloides Michx.) forests in northern Ontario. Individual-tree data from 537 remeasured sample plots were used. This dataset included different combinations of site index, stand density and age. It was possible to compare observations and predictions for different projection length periods. The validation exercise included a biological consistency analysis, the computation of mean percent difference (MPD) for stand density, stand basal area, top height and quadratic mean diameter (QMD) and the comparison of observed and predicted individual-tree dbh. The biological consistency analysis indicated that LS-FVS logically predicted the effect of site index on top height, stand basal area and QMD for black spruce and jack pine. However, the decrease in stand basal area at young ages was inconsistent with the normal development pattern of the forest stands under study and was attributed to deficiencies in the prediction of mortality. LS-FVS was found to underpredict stand density, stand basal area and top height and to over-predict QMD. Even though there were large errors in the prediction of change in stand density, LS-FVS was nevertheless consistent in the prediction of the shape of the dbh size distribution. Key words: FVS, Forest Vegetation Simulator, validation, biological consistency analysis
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".