Long-term effects of a forest amelioration experiment
Bibliographic record
Abstract
We evaluated the soil chemistry, plant species composition, and forest growth rate on a site where a site amelioration project had been realized 30 years earlier. The initial goal of the project was the improvement of a site that had been degraded by litter raking. We wanted to know which amelioration method produced a sustainable result and how different treatments might be rated by today's standards. Treatments included fertilization, underplanting with N-fixing plants, and a combination of both. The amelioration was combined with stand conversion by means of natural regeneration and spruce underplanting. In all treatments, a spruce-dominated stand replaced the secondary pine stand. The biomass of the formerly recalcitrant forest floor (143 Mg·ha1) was reduced by 30 to 50% in treated plots, thereby reducing the total soil pool of C, N, and exchangeable cations. The mineral soil of treated plots was enriched with N, Ca, and Mg. An increase in pH was restricted to the forest floor. The C pool of treated soils was much smaller than that of the control plots. However, the loss from the soil was at least partly offset by increased growth rates of the aboveground tree biomass. In treated plots, the stem volume was more than twice that of control plots (38.3 m3). Soil chemical data and the composition of the ground vegetation suggest that even the control plots have changed compared with pre-treatment conditions. Comparison of different blocks of the experiment suggests that the exclusion of roe deer (Capreolus capreolus) by fencing was the most significant treatment required for successful stand conversion. Prior to fencing, deer browsing inhibited the establishment of a new stand.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".