Discerning responses of down wood and understory vegetation abundance to riparian buffer width and thinning treatments: an equivalence–inequivalence approach
Bibliographic record
Abstract
The combined effectiveness of thinning and riparian buffers for increasing structural complexity while maintaining riparian function in second-growth forests is not well documented. We surveyed down wood and vegetation cover along transects from stream center, through buffers ranging from <5 to 150 m width into thinned stands, patch openings, or unthinned stands of 40- to 65-year-old Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) forests in western Oregon, USA. Small-wood cover became more homogeneous among stream reaches within 5 years following thinning, primarily due to decreases for reaches having the greatest pretreatment abundance. Mean shrub cover converged, predominantly because of decreases in patch openings. Herbaceous cover increased, particularly in patch openings. Relative to unthinned stands, herbaceous cover was similar in wide buffers and increased in the narrowest buffers and in narrow buffers adjacent to patch openings. Moss cover tended to increase in thinned areas and decrease in patch openings. Both conventional point-null hypothesis tests and inequivalence tests suggested that wood and vegetation responses within buffers of ≥15 m width were insensitive to the treatments. However, inherently conservative equivalence tests infrequently inferred similarity between thinned stands or buffers and untreated stands. Difficulties defining ecologically important effect size can limit the inferential utility of equivalence–inequivalance testing.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".