Growth performance, windthrow, and insects: meta-analyses of parameters influencing performance of mixed-species stands in boreal and northern temperate biomes
Bibliographic record
Abstract
Stand structure is a key attribute of forest ecosystems. Mixed-tree plantations are widely felt to be the appropriate option for providing a broad range of goods and environmental services and to reduce susceptibility to natural hazards. However, the debate continues whether mixed plantations can achieve greater financial return than monocultures can. In this study, mixed-species stands of conifers and hardwood species were analyzed in consideration of economically relevant factors. Growth performance and resistance to hazards and pests are widely noted in the literature and are of general economic interest. Thus meta-analyses of relevant studies were conducted to test the following hypotheses: (1) mixing tree species has no significant influence on growth performance or resistance against hazards and pests and, if refuted, (2) mixing tree species causes mainly negative effects on growth performance and resistance against hazards and pests. However, a positive impact of mixing tree species was proven for resistance against windthrow and pests. The meta-analysis on growth performance just as well indicates a positive effect of mixing tree species. Overall, these positive results underscore the need for a large number of additional studies to examine different silvicultural systems to develop optimal management prescriptions to benefit from positive interactions.
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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.031 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".