Humus as an indicator of nutrient availability in a carefully logged boreal black spruce-feathermoss forest in northwestern Québec
Bibliographic record
Abstract
Black spruce (Picea mariana (Mill.) B.S.P.)-feathermoss forests are a common subtype of the northern boreal forests. These forests are associated with large accumulations of mor humus, which is regarded as an important source of nutrients, contributor to soil structure, moisture retention and vital to the long-term sustainability of these forests. Harvesting with protection of advance regeneration (CPRS) is currently used in northwestern Quebec as the method for sustainable management, which reduces soil compaction and protects advance regeneration, and genetic diversity. We examined the effects of CPRS on organic matter and advance regeneration 6 years after harvesting. During the summer of 2002, a humus classification based on observable field characteristics was developed and applied to six CPRS sites in the northern Abitibi claybelt region of Quebec. At each site 75 humus profiles were surveyed and classified by order and thickness of horizons present. Humus horizons were easily observed using morphological features, and master horizon classes were distinguished by their nutritional and biochemical attributes with differences occurring as a result of the natural process of decomposition. Individual humus horizon and total profile thickness was the variable that most affected profile nutrient mass. High forest floor disturbance was associated with shallow profile depth, resulting in low humus profile nutrient mass and low density advance regeneration. Lower forest floor disturbance resulted in deeper profiles associated with higher available nutrients in humus profiles and higher density of advance regeneration. These results suggest that disturbance caused by harvesting may reduce overall stand productivity in the short term due to the effect of low tree density and possibly in the long-term due to loss of nutrients.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".