Yield Responses of Black Spruce to Forest Vegetation Management Treatments: Initial Responses and Rotational Projections
Bibliographic record
Abstract
The objectives of this study were to (1) quantitatively summarize the early yield responses of black spruce ( Picea mariana (Mill.) B.S.P.) to forest vegetation management (FVM) treatments through a meta-analytical review of the scientific literature, and (2) given (1), estimate the rotational consequences of these responses through model simulation. Based on a fixed-effects meta-analytic approach using 44 treated-control yield pairs derived from 12 experiments situated throughout the Great Lakes—St. Lawrence and Canadian Boreal Forest Regions, the resultant mean effect size (response ratio) and associated 95% confidence interval for basal diameter, total height, stem volume, and survival responses, were respectively: 54.7% (95% confidence limits (lower/upper): 34.8/77.6), 27.3% (15.7/40.0), 198.7% (70.3/423.5), and 2.9% (−5.5/11.8). The results also indicated that early and repeated treatments will yield the largest gains in terms of mean tree size and survival. Rotational simulations indicated that FVM treatments resulted in gains in stand-level operability (e.g., reductions of 9 and 5 yr for plantations established on poor-medium and good-excellent site qualities, resp.). The challenge of maintaining coniferous forest cover on recently disturbed sites, attaining statutory-defined free-to-grow status, and ensuring long-term productivity, suggest that FVM will continue to be an essential silvicultural treatment option when managing black spruce plantations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".