Landscape Controls on Structural Variation in Eucalypt Vegetation Communities: Woronora Plateau, Australia
Bibliographic record
Abstract
Quantification of landscape-based vegetation structural variation and pattern is a significant goal for a variety of ecological, monitoring and biodiversity studies. Vegetation structural metrics, derived from airborne laser scanning (ALS or aerial light detection and ranging—LiDAR) and QuickBird satellite imagery, were used to establish the degree of plot-based vegetation variation at a hillslope scale. Topographic position is an indicator of energy and water availability, and was quantified using DEM-based insolation and topographic wetness, respectively, stratifying areas into hot-warm-cold and wet-moist-dry topographic classes. A range of vegetation metrics—maximum and modal canopy height, crown cover, foliage cover, NDVI and semivariance—were compared among randomly selected plots from each topographic class. NDVI increases with increasing landscape wetness, whereas ALS-derived foliage cover decreases with increasing insolation. Foliage cover is well correlated with crown cover (R 2 =0.65), and since foliage cover is readily calculable for whole-of-landscape application, it will provide valuable and complementary information to NDVI. Between-plot heterogeneity increases with increasing wetness and decreasing insolation, indicating that more sampling is required in these locations to capture the full range of landscape-based variability. Pattern analysis in landscape ecology is one of the fundamental requirements of landscape ecology, and the methods described here offer statistically significant, quantifiable and repeatable means to realise that goal at a fine spatial grain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".