Effects of organic matter removal, soil compaction, and vegetation control on 5-year seedling performance: a regional comparison of Long-Term Soil Productivity sites
Bibliographic record
Abstract
We examined fifth-year seedling response to soil disturbance and vegetation control at 42 experimental locations representing 25 replicated studies within the North American Long-Term Soil Productivity (LTSP) program. These studies share a common experimental design while encompassing a wide range of climate, site conditions, and forest types. Whole-tree harvest had limited effects on planted seedling performance compared with the effects of stem-only harvest (the control); slight increases in survival were usually offset by decreases in growth. Forest-floor removal improved seedling survival and increased growth in Mediterranean climates, but reduced growth on productive, nutrient-limited, warmhumid sites. Soil compaction with intact forest floors usually benefited conifer survival and growth, regardless of climate or species. Compaction combined with forest-floor removal generally increased survival, had limited effects on individual tree growth, and increased stand growth in Mediterranean climates. Vegetation control benefited seedling growth in all treatments, particularly on more productive sites, but did not affect survival or alter the relative impact of organic matter removal and compaction on growth. Organic matter removal increased aspen coppice densities and, as with compaction, reduced aspen growth.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".