Incorporation of topsoil and burn-pile debris substantially increases early growth of lodgepole pine on landings
Bibliographic record
Abstract
Rehabilitation of skid trails, temporary roads, and log landings is required for many harvested sites in British Columbia; however, more information is needed regarding practical methods to return these access areas to productive forest. Lodgepole pine ( Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) seedlings (1 + 0) were planted into (i) fully rehabilitated landings (burn-pile debris and topsoil incorporated), (ii) tilled landings, and (iii) unprepared portions of the adjacent cutblock. After two seasons of growth in the field, seedlings planted on fully rehabilitated landings were 38% larger, more robust, and exhibited 44% greater absolute growth and 22% greater relative growth rates, than seedlings planted in the adjacent cutblock. Seedlings planted on tilled landings were 33% smaller, and exhibited absolute growth rates 38% less, and relative growth rates 18% less, than seedlings planted in the adjacent cutblock. Seedlings planted in the cutblock exhibited higher ectomycorrhizal colonization rates. Our results indicate that tillage alone was not sufficient to fully rehabilitate these landings, but that incorporation of recovered topsoil and burn-pile debris produced an excellent growth substrate. This method, using materials found on site, can prevent a significant loss of land regenerating to productive second-growth forest.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".