Geomorphic principles of terrain organization and vegetation gradients
Bibliographic record
Abstract
Abstract. Moisture and nutrient gradients consistently explain much of the variation in plant species composition and abundance, but these gradients are not spatially explicit and only reveal species responses to resource levels. This study links these abstract gradients to quantitative, spatial models of hill‐slope assembly. A gradient analysis in the mixed‐wood boreal forest demonstrates that patterns of upland vegetation distribution are correlated to soil moisture and nutrient gradients. Variation in species abundance with time since the last fire is removed from the gradient analysis in order to avoid confounding the physical environment gradients. The physical‐environment gradients are related to qualitative positions on the hill slope i.e. crest, mid‐slope, bottom‐slope. However, hill‐slope shape can be quantitatively described and compared by fitting allometric equations to the slope profiles. Using these equations, we show that hill‐slope profiles on similar surficial materials have similar parameters, despite coming from widely separated locations. We then quantitatively link the moisture and nutrient gradients to the equations. Moisture and nutrients significantly increase as distance down‐slope from the ridgeline increases. Corresponding vegetation composition changes too. These relationships characterize the general pattern of vegetation change down most hill slopes in the area. Since hill slopes are a universal feature of all landscapes, these principles may characterize landscape scale spatial patterns of vegetation in many environments.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".