Stand-level prediction of balsam fir mortality in relation to spruce budworm defoliation
Bibliographic record
Abstract
Stand production and sustained yield calculations are largely affected by tree mortality, which can be caused by many factors such as competition, insect damage, or climatic events. In the eastern Canadian boreal forest, spruce budworm (Choristoneura fumiferana (Clem.)) defoliation can produce varying levels of mortality in balsam fir (Abies balsamea (L.) Mill.) stands. This mortality was estimated for the entire range of balsam fir in Quebec, Canada, using historical records of insect defoliation and permanent sample plot (PSP) inventories for the 19702003 period, which includes the last insect outbreak. A two-step approach was used to model balsam fir mortality at the stand level. The first step predicts the probability that all balsam fir trees within a PSP will survive during a given time interval. The second step quantifies the amount of mortality for PSP observation periods during which mortality actually occurred. The whole model shows that spruce budworm defoliation may account for between 6% and 100% of the merchantable volume lost due to mortality, depending on outbreak severity. A model evaluation made with an independent data set indicates that the model is unbiased, although the prediction error is relatively large at the stand level but decreases with increasing prediction horizon.
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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) | 0.001 | 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 | 0.000 | 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".