The influence of soil rutting severity on regeneration potential and seedling performance for black spruce-dominated peatlands
Bibliographic record
Abstract
Excessive rutting continues to be recorded in post-harvest compliance inspections in black spruce peatlands across Ontario. These qualitative, visual assessments, however, cannot provide the necessary data to determine a site disturbance threshold that is linked to poor regeneration and seedling growth. In an attempt to provide this quantitative link, harvested black spruce [Picea mariana (Mill.) B.S.P.] peatland sites were surveyed and divided into severely rutted (non-compliant), moderately rutted, and non-rutted (compliant) blocks. In 1995, 2001, and 2006, each block was assessed for areal extent of rutting, number of plantable spots, moisture status, planting medium, competition level, stand density, species composition, and black spruce seedling survival and growth. Black spruce seedling survival in the severely-rutted block was significantly lower when compared with either the moderately rutted or the non-rutted blocks. A derived seedling survival model identified moisture class as having the largest influence on the probability of seedling survival. In terms of seedling growth, however, it was the non-rutted blocks that had the poorest performance. Based on a canonical discriminant analysis (CDA), competition factors, particularly ericaceous shrub cover, were the most important microsite factors influencing black spruce seedling growth. Overall, the moderately rutted block generated conditions that resulted in high conifer recruitment and good seedling survival and growth. Although the inference space of the study is small, the results suggest that black spruce peatland sites may benefit from a moderate level of site disturbance caused during harvest operations. Key words: Rutting severity, black spruce peatlands, survival, growth, microsite assessment
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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 | 0.001 |
| 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) | 0.001 | 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".