Establishment strategies for poplars, including mulch and plant types, on agricultural land in Sweden
Bibliographic record
Abstract
Biomass from forestry is one of the largest components of Sweden's renewable resources. Poplars are currently the highest producing tree species available and are therefore natural choices for biomass-oriented production. Growing poplars has been of most interest on agricultural land, but the knowledge and experience about their cultivation is still limited. Factors that have a large impact on the regeneration results are plant material, competing vegetation, browsing and damage caused by voles or climatic factors. Due to large establishment costs, there is a need to find methods to secure the establishment both biologically and economically. In this study the effect of plastic mulch in combination with three different plant types (short cuttings, long cuttings and rooted plants) were tested at three different sites. Five years after planting, the overall effect of mulch was an improved plant survival and growth. In most cases, long cuttings outperformed short cuttings and rooted plants. Clonal differences were present, indicating the importance of using plant material adapted to site conditions. All sites were heavily affected by browsing and during the experimental period 100% of the plants were damaged at some point. Planting poplars without fencing is therefore doubtful. Results from this study conclude that poplars can be established with success on agricultural land if proper measures are used depending on the site to be planted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".