Germination and establishment of forest sedges (<i>Carex</i>, Cyperaceae): tests for home‐site advantage and effects of leaf litter
Bibliographic record
Abstract
We investigated aspects of germination and seedling establishment that might influence the distribution and diversity of Carex species growing in the forest understory. We tested the ability of Carex communis and C. platyphylla to germinate and establish at plots where adult individuals of one of these two species, or one of two other Carex species (C. backii and C. plantaginea), occurred in an old-growth forest in southern Québec, Canada. We also tested for effects of leaf litter on germination and establishment of these sedges. From a series of experiments in the field and in a lath house, we found no evidence of home-site advantage with respect to germination or seedling establishment. Leaf litter had a negative effect on germination and establishment. The results emphasize the importance of dispersal and colonization events in determining local diversity and distribution of Carex species in upland forests. High frequency of occurrence of C. communis at our study site may result from relatively wide dispersal provided by ants, and a suite of traits associated with ant dispersal in some understory Carex species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".