Deep planting of Norway spruce seedlings: effects on pine weevil feeding damage and growth
Bibliographic record
Abstract
As the use of mounding as a soil preparation method and mechanized planting become more common, the use of deep planting has increased. In deep planting, a greater portion of the stem is buried below the soil surface. However, it is feared that this increases the risk of insect damage, especially damage from the pine weevil Hylobius abietis (L.) (Coleoptera: Curculionidae). The effects of planting depth on the feeding preference of adult pine weevils in 1.5-year-old Norway spruce (Picea abies (L.) Karst.) seedlings were investigated using choice experiments. Seedlings were planted at two depths in a pot to compare (i) normal planting depth (target depth 3 cm) and (ii) deep planting (8 cm). In deep-planted seedlings, the number of feeding scars on lower stem parts above the soil surface, as well as severe feeding, was effectively reduced. After planting, the aboveground portion of the stem in deep-planted seedlings was clearly shorter and thinner, but the stems grew more both in height and diameter during the 5-week growing period than did normal-planted seedlings. At the end of the experiment, the deep-planted seedlings were still shorter, but there was no difference in diameter. In conclusion, deep planting protected 1.5-year-old seedlings from pine weevil feeding and improved seedling growth.
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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) | 0.001 | 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 | 0.001 |
| 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".