THE EFFECTS OF MICROTOPOGRAPHY AND ORGANIC LAYER THICKNESS ON SEEDLING HEIGHT GROWTH AT DIFFERENT SPATIAL SCALES IN BLACK SPRUCE STANDS PRONE TO PALUDIFICATION
Bibliographic record
Abstract
In the eastern boreal forest of Canada, growth of black spruce (Picea mariana (Mill.) BSP) can be impeded by paludification which occurs in the later stages during stand development, or when the microtopography is undulating. Since black spruce forest stands occur at broad scales on flat surfaces, it is difficult to determine the origin of paludification in these stands. The main objectives of our study were: 1) to assess whether black spruce height growth, thickness of the organic layer (OL) and microtopography are spatially structured at fine and large scales, and 2) to determine if OL thickness affects height growth and if microtopography influences the accumulation of organic matter at different spatial scales. Our results showed that black spruce height growth was autocorrelated to a greater extent in the wildfire (103 m) site than in the harvesting (43 m) site. The spatial structure of OL thickness was similar in both sites. An undulating microtopography in the harvested site, as illustrated by spatial variance in relative elevation at short distances (approx. 50 m), negatively affected the accumulation of organic matter at distances less than 20 m. The accumulation of organic matter in hollows created permanent poor conditions for black spruce growth indicating that paludification originated from a toposequence in the harvesting site. In contrast, organic matter likely accumulated as a result of paludification during stand development in the wildfire site where there was no spatial structure for relative elevation and no relationship between relative elevation and OL thickness. Because the origin of paludification affects soil substrate quality, forest management strategies will differ according to the origin of paludification. Paludification that occurs during stand development can be more easily controlled and reversed by silvicultural treatments such as prescribed burning and 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".