Effect of vegetation control on the survival and growth of Scots pine and Norway spruce planted on former agricultural land
Bibliographic record
Abstract
The effects of competing vegetation and various weed control methods (fibreboard mulch, cover crop of clover, and various herbicides) on the survival and growth of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) seedlings were compared based on 6- to 11-year data from a field experiment established as a randomized block design with three replications in southern Finland. The most effective herbicides significantly reduced the weed cover for 2 or 3 years. The mortality, basal diameter, height, and volume of Scots pine significantly correlated with percent cover of the ground vegetation. In the case of Norway spruce, only the stem diameter correlated significantly with percent cover of the competing vegetation. This was probably due to the severe frost damage that occurred in the third growing season. The mortality of pine began to increase only when the vegetation cover had exceeded 60%. After 11 years, the stand volume on the pine plots treated with terbuthylazine was almost double that of the untreated plots (32 vs. 17 m3·ha–1), but this difference was not statistically significant. Mulch and cover crop did not significantly affect pine growth or mortality. Recurrent frost damage may explain why none of the studied treatments significantly affected the mortality and volume growth of Norway spruce.
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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) | 0.001 | 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".