Plant Abundances after Clearcutting and Stripcutting in Central Labrador
Bibliographic record
Abstract
Abstract We compared plant abundances between clearcuts (n = 10) and stripcuts (n = 6) on former Picea mariana sites harvested in the mid-1970s and mid-1990s in central Labrador, Newfoundland, and Labrador, Canada. Redundancy analysis (RDA) found logging methodsan important determinant of conifer abundance for 1990s logging, showing P. mariana associated with clearcuts and Abies balsamea associated with stripcuts. Our RDA of the years combined found logging methods unimportant, but the year of logging was the most important factor followedby pH and drainage. The size distribution of trees, with the exception of Alnus rugosa, increased with stand age. Geocaulon lividum and Cladina arbuscula were associated with 1970s logging and coarse woody debris, Vaccinium vitis-idaea, and Cornus canadensis were associated with 1990s logging. Sphagnum spp. was positively associated with imperfectly drained sites and high pH, and Pleurozium schreberi was positively associated with moderately drained sites. Our results suggest only a short-term effect of logging methods on regeneration, and similarities may have resulted from the small opening sizes and irregular shapes of our clearcuts. We suggest that stripcutting to promote P. mariana regeneration may offer little, if any, benefit over clearcutting when distances between forest canopies within clearcuts are typically 300 m or less.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".