Heavy Grazing of Canadian Bluejoint to Enhance Hardwood and White Spruce Regeneration
Bibliographic record
Abstract
Abstract Wet, disclimax stands of Canadian bluejoint (Calamagrostis canadensis [Michx.] Beauv.) created by logging were heavily grazed by cattle and horses years 5 through 8 after logging to weaken the grass and favor regeneration of hardwoods and white spruce (Picea glauca [Moench.] Voss). Seedling densities of hardwoods and white spruce in heavily grazed stands were not significantly different (P < 0.05) from those in ungrazed stands. Heavy grazing reduced herbaceous cover and litter but was not detrimental to runoff water quality. Heavy grazing was not effective for increasing regeneration in wet disclimax stands of Canadian bluejoint where the grass had already increased following overstory removal, but earlier application and use in drier sites should be considered. North. J. Appl. For. 18(1):19–21.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".