The irregular shelterwood silviculture system and managing for stand complexity from a North American perspective
Bibliographic record
Abstract
Structurally and compositionally, diverse forests are associated with increased resilience and provision of desirable ecosystem services. Such forests can be created and maintained by silvicultural systems that foster natural regeneration, emulate natural disturbances, and promote the development of old growth features. Irregular shelterwood is increasingly applied to fill this niche. However, the implied meanings of both “stand irregularity” and the “irregular shelterwood system” are varied across sources, resulting in inconsistent application in forestry research. We revisited definitions of the term “irregular shelterwood,” reviewed studies of its applications, and examined it in the broader context of natural disturbance ecology and forest complexity, with a focus on the North American silvicultural perspective. We found two irregular shelterwood approaches – group-based and two-aged – that share conventional shelterwood traits. Both approaches produce multiage stands. We also found approaches to irregular shelterwood that resemble types of selection or that are non-systematic. Inconsistent terminology and naming conventions were evident across all approaches. These variable and contrasting portrayals inhibit the synthesis of study results by researchers and forestry practitioners. To clarify these ambiguities, we identified the features that best characterize the different types of partial cuttings reflected in the irregular shelterwood literature. • Group-based and two-aged irregular shelterwoods show typical shelterwood traits. • Other irregular shelterwood representations are more selection-like. • These various approaches can achieve disturbance emulation and structural objectives. • Inconsistent terminology complicates understanding of silvicultural methods. • Consistent reporting of silviculture treatment features is needed for future analyses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".