Modeled diameter growth response to intermediate treatments of planted white spruce (<i>Picea glauca</i>) affected by eastern spruce budworm (<i>Choristoneura fumiferana</i>) in Minnesota, U.S.A.
Bibliographic record
Abstract
White spruce (Picea glauca (Moench) Voss) plantations have historically been an important source of high-quality forest products in the Great Lakes Region of North America. Thinning in spruce plantations is a common silvicultural practice for reducing competition and promoting resiliency to forest health threats such as eastern spruce budworm (Choristoneura fumiferana Clemens), a native forest pest of eastern North America. Spruce budworm larvae feed on the foliage of trees, which reduces growth and potentially causes mortality during an outbreak. There has been continual spruce budworm defoliation in northern Minnesota, U.S.A., since the mid-1950s, with higher levels of defoliation in the late 1990s. This research modeled the diameter growth response of white spruce 18 years after initial thinning in stands that presently range between 44 and 64 years old. Some stands received a second thinning in recent years. We used generalized nonlinear least squares and nonlinear mixed-effects models to estimate annual diameter growth using common tree and stand metrics. Growth model performance was improved by including thinning and frequency of spruce budworm defoliation as modifiers of diameter growth. Results of this study highlight how thinning in combination with insect disturbance affect diameter growth in white spruce plantations of northern Minnesota.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".