Spatial Relationships between Leaf Area Index and Topographic Factors in a Semiarid Grassland: Joint Multifractal Analysis
Bibliographic record
Abstract
A considerable portion of Canada’s landmass is covered by grassland ecosystems. Insight into the grassland spatial heterogeneity will not only contribute to better understanding of the scale dependent ecological processes but will also help in management and monitoring. Leaf area index (LAI) is a key structural attribute of grassland that reflects primary production. It is well-known that topography controls grassland productivity and heterogeneity but little is known which topographic index correlates best with LAI at multiple scales. In this study, we have used multifractal and joint multifractal techniques to investigate how leaf area index in a semiarid grassland is linked with topographic factors at multiple scales. The topographic indices assessed in this study were wetness index, upslope length, and relative elevation. Our results show that field LAI is significantly correlated (P < 0.01) with the studied topographic factors and the effect of topography on grassland primary productivity is better explained by wetness index than upslope length or relative elevation. LAI, wetness index, and upslope length are multifractally distributed whereas distribution of relative elevation is monofractal. Joint multifractal analysis shows that the relationships between LAI and topographical factors are highly scale dependent, however, LAI is weakly correlated to relative elevation. Overall, this study suggests that the effect of topography on bioproductivity should be considered at multiple scales and multifractal and joint multifractal techniques are particularly useful in elucidating multi-scale spatial patterns of grassland ecosystems.
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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 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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 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".