Species and Spacing Effects of Northern Conifers on Forest Productivity and Soil Chemistry in a 50-Year-Old Common Garden Experiment
Bibliographic record
Abstract
Abstract This study examines the long-term effects of experimental conifer monocultures on stem volume and soil chemistry. Replicated plots of black spruce ( Picea mariana ), white spruce ( Picea glauca ), and red pine ( Pinus resinosa ) were planted in 1950 at three spacings (1.8, 2.7, and 3.6 m) on a briefly cultivated agricultural field on glaciolacustrine sandy loam soil near Thunder Bay, Ontario, Canada. Stem volumes per hectare and per tree were measured in 2002 (52 years after planting). Surface organic (i.e., forest floor) and mineral soil fertility in terms of pH, total N and P, and extractable NH 4 -N and P were measured in 2000 (50 years after planting). Of the three species red pine had the highest volume per hectare at all the three spacing followed by white spruce and black spruce; volume for all three species peaked at the 1.8-m spacing. The effects of conifer species on soil physical and chemical properties were more pronounced than spacing effects and the changes were mainly confined to forest floor layer. Few changes in mineral soil properties occurred because of the species and spacing treatments. Per hectare forest floor nutrient pools were higher under white spruce and red pine than black spruce, a pattern likely driven by higher litterfall and forest floor accumulation. It appears that toward the end of first rotation the higher productivity of red pine compared with the other conifers did not come at the cost of reduced soil pools of available NH 4 , PO 4 , or K, but it was associated with reduced Ca levels in the forest floor.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".