Plant species traits across a riparian‐zone/forest ecotone
Bibliographic record
Abstract
Abstract. We examined the changes in prevalence of nine plant traits – including the presence of woody stem tissue, leaf longevity, nitrogen fixation, seed longevity, dispersal vector, pollination vector, and clonal growth form – across a riparian/forest‐understory ecotone. This ecotone, found along headwater streams in boreal mixed‐wood forests, supports four distinct vegetation zones: streambank, riparian, transition, and upland forest understory. The objective of this study was to identify specific trait patterns that may indicate functional responses to the changes in environmental factors such as nutrient availability and wind exposure that occur across the ecotone. The suites of plant species traits found in each zone were distinct, with a strong change in the prevalence of several traits. Wind and insect pollination, wind and vertebrate diaspore dispersal, and deciduous and evergreen leaves showed the greatest change in prevalence between the vegetation types. Some traits, including insect pollination and vertebrate diaspore dispersal, were strongly correlated within species. The consistent cooccurrence of pairs of traits in the same species suggests common responses by very different traits to the same environmental factor. This study demonstrates that an ecotone can be characterized not only as a discontinuity in species distributions or environmental factors, but also as a discontinuity in the trait spectrum. Examining ecotones from a trait perspective has strong potential for identifying the environmental factors and associated species functional responses that encourage the development of distinct vegetation boundaries.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".