Einfluß einwandernder Espen (Populus tremuloides) auf den Stickstoffhaushalt nordamerikanischer Prärieökosysteme
Bibliographic record
Abstract
Variables that contribute to a higher soil mineral nitrogen content in forest soil than in adjacent grassland soil were studied in a Canadian forest-grassland ecotone. For this, the correlations of soil mineral nitrogen with tree age, soil moisture, air temperature and light penetration were studied along transects extending from the centre of aspen groves (Populus tremuloides) 5 m far into the prairie. Among all correlations only the increase of nitrogen content with decreasing soil moisture was significant. The results indicate that nitrogen uptake by plants and microbes in the forest is limited at a higher level of soil moisture than plants and microbes in the grassland. As an alternative explanation I have tested the assumption that, because of canopy shading, litter decomposition in forests is faster than in adjacent grassland and that therefore the mineral nitrogen content of forest soil is higher than that of grassland soil. For this, the effects of litter type (aspen or grass mix), habitat type (forest or grassland), and shading (with or without artificial shading) on the rates of litter mass loss and nitrogen release were investigated Previous year"s litter was collected in April and put in 1 dm2 large litter bags in portions of 2 g. The bags were laid out in the experimental plots in the first week of May. Bags containing ion-exchange resin were put under the litter bags to measure nitrogen release. Uncovered resin bags were used to measure nitrogen deposition. One fourth of the litter bags and resin bags were each retrieved after 4, 9, 16, and 21 weeks. Decomposition rates were calculated from litter mass loss measurements. Nitrogen release measurements were converted to rates of nitrogen retention and nitrogen loss. Grass litter (k=-1.36) decomposed signifcantly faster than aspen litter (k=-0.44). Decomposition of both litter types was fastest in unshaded prairie plots, thus there was a significant habitat type x shading interaction. Grass litter captured less and released more nitrogen from deposition than aspen litter. An analysis of total N content of litter showed that when deposition was accounted for both litter types had lost nitrogen. This nitrogen was probably lost by fragmentation and not by mineralization. Aspen litter decomposed slower than grass litter, even in shade. Thus, decomposition is rejected as an explanation for the higher nitrogen content in forest soils, but the first explanation should be retained.
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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 | 0.000 | 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) | 0.001 | 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".