Soil Quality and Tree Growth in Plantations of Forest and Agricultural Origin
Bibliographic record
Abstract
Soil organic matter loss and increased soil compaction have been identified as the factors most likely to directly impact tree growth in managed forests. We compared the soil quality of plantations established on former agricultural lands ( n = 20) with plantations established following clear cutting of native forests ( n = 20). Half of the plantations had been planted with jack pine ( Pinus banksiana Lamb.) and half with white spruce [ Picea glauca (Moench) Voss], 9 to 27 yr before the study. The old field plantations had lower (at 0–10 and 10–20 cm) mineral soil macroporosity and higher field capacity than forest plantations, indicating more severe soil compaction. The old field plantations, however, also had higher soil C content, raising the permanent wilting point and canceling compaction effects on the available water holding capacity. An indicator of organic matter quality, namely the potential net mineralization per unit of soil Kjeldahl N, was lower in the old fields. Species also affected soil quality indicators—with lower values of macroporosity and higher values of field capacity observed under white spruce. Despite significant differences in soil conditions, no significant effect ( P < 0.05) of plantation origin on tree growth could be found. Old fields can support productive plantations of both species.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".