Restoration of floral diversity through plantations on abandoned agricultural land
Bibliographic record
Abstract
Plantations have been claimed to be "monocultures", or "biological deserts". We investigated these claims in the context of a long-term study on plant diversity within plantations with different indigenous tree species, spacings, and soil types that were compared with 410 native stands. Soil type had no influence on plantation species diversity or abundance, and wider spacing resulted in higher richness, lower woody plant abundance, slightly higher cover of herbaceous plants, and large increases in cryptogam cover. We also found a canopy species × spacing interaction effect, where the impact of increased spacing on understory vegetation was more pronounced in spruce than in pine plantations. The dynamic community interactions among species of feathermoss appear to be in response to the physical impediment from varying amounts of needle rain from the different tree species. High light interception and needle fall were negatively correlated with understory plant diversity, as was lack of structural diversity. This study indicates that through afforestation efforts agricultural lands can be restored to productive forests that can harbour nearly one-half of the plant species found in equivalent natural forests within the same geographic region in as little as 50 years. We recommend applying afforestation using indigenous conifer species as a first step towards rehabilitating conifer forests that have been converted to agriculture and subsequently abandoned.
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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 | 0.001 |
| 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) | 0.000 | 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".