Vegetation changes in boreo–nemoral forest stands depending on soil factors and past land use during an 80 year period of no human impact
Bibliographic record
Abstract
Information on the long-term changes in plant communities that occur without human interference is limited, due to insufficient studies where vegetation can be resurveyed. In 1912, a strict nature protection reserve, with non-intervention management, was established on Moricsala Island in Latvia, located in the boreo–nemoral forest zone. Prior to establishment of the nature reserve, part of the island area was used for agriculture. The island is now covered almost entirely by forest dominated by Quercus robur L. and Tilia cordata Mill. on sandy soils. Resurvey was conducted in 2011 in 17 plots in which tree layers and the understory vegetation had been described in 1930. The plots were classified into two groups based on tree species composition in 1930 to determine changes in species composition. Differences in understory and tree canopy composition between these groups persisted over the period between the studies, in relation to soil factors and past stand history. Considerable turnover of species occurred, with a decrease of species typical of open habitats and replacement by species typical of nemoral (temperate deciduous) forest plant communities. In the past, Q. robur had likely established in open habitats created by agricultural land use or past fire but is presently not regenerating in the understory.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".