Growth and survival dynamics of partially cut northern hardwood stands as affected by precut competition and spatial distribution of residual trees
Bibliographic record
Abstract
Abstract Modelling growth and survival dynamics after partial harvesting must take account of the heterogeneous spatial pattern of residual trees that results from the presence of machinery trails. We used data from 23 permanent sample plots in northern hardwood stands to reconstruct the growing environment of individual trees before and after partial harvesting. We modelled harvest probability, growth response and survival probability using a complementary set of explanatory variables that was assembled to reflect the spatial distribution of trees and skid trails prior to and after harvest. Results showed that the distribution of harvested trees was concentrated in skid trails and in their close vicinity. However, this spatial pattern had no significant effect on either the post-cut basal area increment (BAI) or the survival of residual trees. BAI and survival of individual trees were both mostly related to the competitive environment prior to harvest, while post-cut changes in competitive environment had only a marginal effect on growth and survival dynamics. We conclude that selection cuts did not substantially increase the growth and survival of residual trees, likely because tree removal was mostly concentrated near skid trails, where the negative effects of machinery access were highest.
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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 | 0.000 |
| 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 | 0.001 |
| 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".