Germinability of arctic plants is high in perceived optimal conditions but low in the field
Bibliographic record
Abstract
Sexual reproduction is crucial for plant populations to track and adapt to climate change, but it is uncertain to what degree arctic vascular plants reproduce by seed. Several studies on arctic species show low germination. To re-examine seed germination and evaluate factors limiting sexual reproduction, seeds of 6–22 arctic species were germinated in five different, increasingly more realistic, conditions. Thirteen out of 15 species that were tested in an earlier study in Svalbard, Norway, germinated better in our study. Compared with perceived optimal conditions in a growth chamber, average germination per species was 6%–52% lower in five out of six species germinating at a colder temperature in soil, 36%–64% lower when germinating outdoors in soil, 49%–91% lower when germinating in a moss covered moraine, and 55%–91% lower when germinating in open soil on a moraine. Germination outdoors was below 5% in 10 out of 13 species and not correlated to germination in perceived optimal conditions. The high germination compared with earlier studies suggests that climate warming has already increased seed viability. However, caution should be taken when evaluating species-recruitment potential based on laboratory studies, as germination in the field was limited by species-specific responses to low temperatures, moisture, predation, and safe-site availability.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".