Early field performance of sand-coated (Conniflex®) and insecticide-treated Norway spruce seedlings planted on spot mounds and undisturbed soil with and without the addition of arginine phosphate (ArGrow®Granulat) nutrition
Bibliographic record
Abstract
In Nordic countries, mechanical site preparation is used to enhance seedling growth and protect them against pine weevil ( Hylobius abietis (L.)) damage. However, there is growing demand to reduce forest regeneration costs and harmful environmental impacts of site preparation. This has increased interest to explore alternative methods. Arginine phosphate nutrition may provide an alternative to intensive site preparation to improve growth. Still, in undisturbed soil, some protection against pine weevil is needed. In this study, we evaluated the early field performance of sand-coated (Conniflex®) and insecticide-treated (lambda-cyhalothrin, KarateZeon) Norway spruce ( Picea abies (L.) Karst.) container seedlings planted on spot mounds and in undisturbed soil with and without the addition of arginine phosphate (ArGrow®Granulat) nutrition. Over two growing seasons, spot mounding improved the height and diameter growth of the seedlings and reduced the pine weevil damage compared to undisturbed soil. Arginine phosphate increased the first-year height and diameter growth in KarateZeon-treated seedlings and second-year height growth of the Conniflex-treated seedlings but otherwise had no significant effects on damage or survival. Both protection methods offered similar level of protection. Based on our findings, the use of mechanical site preparation is still recommended, particularly in areas with high pine weevil feeding pressure.
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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) | 0.001 | 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".