Spatial patterns in defoliation and the expression of clonal traits in grazed meadows
Bibliographic record
Abstract
Clonal plant species dominate meadow vegetation where grazing can generate spatial heterogeneity at different scales and can select for species that express particular sets of clonal traits. This in situ study aimed to characterize fine-grained spatial patterns of defoliation (<1 m) induced by contrasting cattle grazing intensities and to link these spatial patterns with the abundance of species-specific clonal traits. Using correlogams and synthetic spatio-temporal indices, the heterogeneity of vegetation height and leaf damage was monitored along a cattle grazing gradient. Species were identified and their clonal traits retrieved from the database CLO-PLA3. Under moderate grazing, fine-grained spatial patterns of defoliation were not stable over time. Defoliation was heterogeneous during the first months of the grazing season and then became homogeneous. Intensive grazing generated homogeneous defoliation, regardless of the date. In the study meadow, grazing gave rise to communities containing a greater abundance of annual species. However, clonal traits assumed to enable clonal fragments to benefit from heterogeneity do not seem advantageous. Increasing grazing intensity promoted species with clonal traits expected to minimize costs associated with clonality (aboveground clonal growth forms, short-distance lateral spread, and (or) short-lived connections). Ungrazed conditions favoured species with clonal traits associated with a high competitive ability.
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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 | 0.000 |
| 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".