A spatial approach to soil‐ecological experimentation at landscape scale
Bibliographic record
Abstract
Abstract The upscaling of soil‐ecological processes to larger landscape units represents a special challenge to soil ecology. Results from micro‐ or mesocosms cannot easily be transferred to other scales because effects are often scale‐dependent. In this context, field experiments which take into account the heterogeneity of the landscape may be promising. Therefore, we carried out an experiment based on a transect study in the agrolandscape of NE Germany on heterogeneous sandy soil in which the feeding activity of the soil‐organism community was assessed by means of the bait‐lamina test at each of the 101 transect locations. At every 4th position, prior to the measurement the soil biota were stimulated by a treatment consisting of adding easily available C and water to the soil. Our aim was to test whether this kind of spatial approach enables to separate effects induced by treatments from landscape effects. The results showed a highly variable feeding activity along the transect after 4 weeks. Despite this variability, a basic trend could be identified which was related to a landscape factor,i.e., the relief. On upper‐slope positions, the feeding activity tended to be less in comparison to positions down‐slope. At every 4th position of the transect, the stimulating effect of the substrate and water addition could be clearly detected and quantified with spectral and cross‐spectral analysis. It is concluded that effects of treatments in heterogeneous landscapes may be distinguished from site effects when the signal‐to‐noise ratio is high and soil and treatment effects on the variable of interest are sufficiently different from one another. In a heterogeneous landscape with gradients of site properties, a treatment based on the frequency domain and applied in regular intervals can be distinguished with spectral analysis techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".