Analytical methods for defining standclearcut edge effects demonstrated for N mineralization
Bibliographic record
Abstract
Edge effects are becoming an important forest management consideration, but information regarding the influence of edges on N cycling variables has not been well documented. In addition, the quantification of edge effects can benefit from the application of complementary spatial analysis methods. Forest floor N mineralization and environmental variables were intensively measured 5 years after harvest along transects crossing the north and south edges of a 1-ha clearcut, in a high-elevation Engelmann spruce subalpine fir forest. Wavelet analysis and depth-of-edge influence (DEI) methods were used to locate and measure the spatial extent of edge effects on N mineralization. Then variance partitioning (partial redundancy analysis) was used to examine the influence of edges on N mineralization relative to the influence of other environmental factors. Initial NO 3 -N content and net nitrification markedly increased in the opening within 26 m of each edge. Net ammonification did not exhibit obvious edge-related spatial patterns. Spatial patterns of nitrification appeared to be more closely related to spatial changes in substrate quality than to soil temperature and moisture. Results of the wavelet and DEI analyses provided quantification of locations and functional extents of edge effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| 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 teacher head, 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".