A simple germination protocol for<i>ex situ</i>propagation of the endangered<i>Carex lupuliformis</i>
Bibliographic record
Abstract
Plant reintroductions have become an important component of species recovery strategies. To favor establishment and survival rates of reintroduced specimens, the use of mature individuals is often recommended. Producing individuals from seed can be challenging, because little is known about the germination requirements of many endangered species. Here, we investigated whether Carex lupuliformis achenes can be germinated at high rates under semi-controlled ex situ conditions. More specifically, we aimed to determine which simple stratification technique allows higher/faster germination rates, whether scarification speeds up the germination process, and which light intensity allows higher/faster germination rates. We found that a brief cold-wet stratification (one month in wet sand) increases the likelihood that C. lupuliformis achenes will germinate, but that a similar germination rate can be obtained by storing achenes at 4°C for six months in a plastic bag. Although scarification did not affect final germination rates, scarified achenes germinated significantly faster than unscarified ones. Finally, we found that a light intensity of 25% resulted in significantly higher final germination rates than lower light intensities. In conclusion, our experiments showed that C. lupuliformis is easy to propagate ex situ, as a variety of treatments resulted in relatively high germination rates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".