RELATIONSHIPS BETWEEN GROWTH SUBSTRATES AND THE GROWTH AND NUTRITION OF YOUNG BLACK SPRUCE ON POST-DISTURBED LOWLAND BLACK SPRUCE SITES IN EASTERN CANADA
Bibliographic record
Abstract
Forested peatlands represent an important timber resource in eastern Canada.These sites are generally harvested with careful logging often conducted during winter when the soil is frozen. The aim of this harvesting method is the protection of soils and of advanced regeneration. In spite of a general appreciation, one calls in to question the use of this method in certain areas because growth problems have been observed, particularly in black spruce-feathermoss stands prone to paludification. The main objective of this study was to compare the quality of growth substrates for black spruce growth in lowland black spruce stands regenerating from either careful logging or wildfire. The comparison of growth substrates has been performed in the field and in a controlled environment. The results from the retrospective study suggest that black spruce seedlings height growth is greater with substrates made of feathermosses, fibric material of feathermoss origin, as well as mixture of fibric and humic materials than with fibric Sphagnum, mineral soil and decaying wood. The most favourable substrates are characterized by better black spruce N and P foliar status. The preliminary results from the controlled experiment showed better growth with substrates made of fibric material of feathermoss origin (after careful logging and wildfire) and Sphagnum. Based on these two experiments, we conclude that in order to maintain or increase black spruce productivity following careful logging of sites prone to paludification, plantation in substrates originating from feathermosses and management techniques that could promote forest mosses to the detriment of Sphagnum mosses should be developed.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".