Refining the Forest Vegetation Simulator for projecting the effects of spruce budworm defoliation in the Acadian Region of North America
Bibliographic record
Abstract
The Forest Vegetation Simulator (FVS) is an individual-tree growth model widely used in the US and parts of Canada, which has been developed to predict stand dynamics in response to various disturbance-causing agents. In this study, we developed annualized modifiers for the Acadian variant of FVS (FVS-ACD) to account for effects of spruce budworm (SBW), Choristoneura fumiferana (Clem.), defoliation on forest development. These modifiers were developed using individual tree data collected annually from 375 permanent sample plots that covered > 40 000 km2 of varying ranges of defoliation and forest conditions during the last SBW outbreak in the 1970s-1980s in Maine. FVS-ACD refined by our modifiers consistently had smaller mean biases (predicted – observed; 64% and 79% lower in Maine and New Brunswick, respectively) and root mean squared errors (RMSE; 26% lower in both Maine and New Brunswick) of predicted stand volumes than FVS-ACD refined by STAMAN (a Canadian growth model) SBW modifiers, which was superi...
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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 | 0.001 |
| 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 | 0.000 |
| 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".