Effect of soil physical properties on the long-term performance of planted Scots pine in Finnish Lapland
Bibliographic record
Abstract
Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies L. Karst.) forests dominate in Finnish Lapland. This study examined the long-term effects of soil physical properties and conditions measured in intact intermediate areas, as well as site preparation, on the survival and height growth of planted pine on eight experimental sites, 25-27 yr after reforestation. On the four originally spruce-dominated sites, pine survival was the highest on sites with a high soil air-filled porosity (AFP) near saturation (at -1 kPa), a high van Genuchten parameter, and a low soil water content (SWC) in situ, and height growth was the fastest on sites with a high soil AFP in situ and a high van Genuchten parameter n, and on sites reaching a soil AFP of ca. 0.20 m3 m-3 at a high matric potential after saturation. Survival, but not mean height, was enhanced on the spruce sites by intensive site preparation methods such as ploughing instead of lighter site preparation methods. On the four originally pine-dominated sites, site preparation affected the mean height but not survival. The use of SWC as a sole criterion for sites suitable for pine reforestation was tested and found to be uncertain. Key words: Boreal forest soils, soil water content, air-filled porosity, van Genuchten function, site preparation, reforestation, Scots pine
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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 | 0.000 | 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) | 0.000 | 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".