Silvicultural intensification and the impact on structural diversity in a regenerating Canadian boreal mixedwood
Bibliographic record
Abstract
Silvicultural intensification is being implemented across the boreal mixedwood forest to increase timber productivity, but there are concerns about reducing the complexity and heterogeneity of stands. To investigate this, 20th year post-harvest data from the NEBIE site (Natural, Extensive, Basic, Intensive, and Elite, referring to an increasing gradient of silvicultural intensity) near Timmins, Ontario was used. Natural was unharvested, Extensive was naturally regenerated, and Basic, Intensive, and Elite had site preparation, white spruce planted, and herbicide applied in increasing intensity. The treatments were applied operationally to 100 m x 200 m experimental plots. All treatments were effective at creating stands with multiple species with Basic having the highest tree species diversity, followed by Intensive, Elite, and Extensive. Basal area and densities were similar in treated stands, although how it was distributed among tree species and sizes was different, with the proportion of coniferous trees and tree size increasing with intensity. Spatially, all treatments showed an aggregated arrangement of trees, with none showing a regular or uniform arrangement. Spatial interactions between broadleaf and coniferous species reflected the past silvicultural prescriptions with Elite and Basic showing attraction between coniferous and broadleaf species, Intensive showing a repulsion, and Extensive having a random pattern. Overall, Elite created an intimate mixture of conifers and broadleaves and was most similar to the Natural forest in terms of species composition and spatial arrangements. These results indicate that silvicultural intensification can be applied in a manner that does not reduce structural complexity in this forest type.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".