Black spruce and vegetation response to chemical and mechanical site preparation on a boreal mixedwood site
Bibliographic record
Abstract
The growth and development of outplanted black spruce (Picea mariana (Mill.) BSP) and competing vegetation five growing seasons after mechanical and chemical site preparation treatments are presented. The largest stem volume increase for black spruce coupled with the lowest vegetation indices for competing trees and shrubs were recorded on the treatment consisting of chemical site preparation with liquid hexazinone applied at 3.1 kg active ingredient (a.i.)·ha-1 followed by chemical tending in the second and fourth growing season with glyphosate applied at 1.78 kg a.i.·ha-1. Black spruce stem volume growth was second highest and the vegetation indices for competing trees and shrubs the highest, on plots treated with hexazinone site preparation. Among mechanical treatments, black spruce stem volume was highest on plots treated with mixed-mound site preparation. No other mechanical site-preparation treatment improved the growth of black spruce over boot-screef site preparation alone. The vegetation index of trembling aspen (Populus tremuloides Michx.) was reduced on mixed-mound and area-mixed site preparation treatments. The vegetation index of red raspberry (Rubus idaeus L.) was reduced on area-mix and area- and strip-screef treatments. By the fifth growing season, site-preparation treatment had little effect on the comparative growth of grasses and forbs. High-speed strip-mixing with 80 cm wide strips spaced at 2-m centres, on deep, fertile, silty loams of Site Region 3W-Lake Nipigon, does not appear feasible as an alternative to chemical site preparation or conventional manual and mechanical site preparation.
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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 | 0.000 | 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".