Vegetationenvironment relationships in zero-order basins in coastal Oregon
Bibliographic record
Abstract
Zero-order basins, where hillslope topography converges to form drainages, are common in steep, forested landscapes but we know little about their ecological structure. We used indirect gradient analysis to characterize gradients in plant species composition and cluster analysis to characterize groups of plant species associated with specific geomorphic areas. We sampled vegetation within 63 randomly selected zero-order basins in the southern Coast Range of Oregon and collected data on herb, shrub, and overstory tree cover, as well as environmental conditions. Zero-order basin overstories were similar in tree composition to both first-order riparian and upland plant assemblages, but were intermediate in tree density. Shrubs in zero-order basins included both species associated with dry upland conditions and species associated with riparian conditions. Results suggest that understory plant species composition in zero-order basins follows gradients in geomorphic and overstory conditions. Furthermore, it appears that zero-order basins have distinctive geomorphology and fluvial regimes. These distinctive features appear to support both plant species associated with riparian conditions and species associated with upland conditions. Zero-order basins represent the farthest upstream extension of riparian plant species into upland areas, increasing plant species diversity in steep, forested landscapes.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".