Growth and Reproductive Responses of <i>Cassiope tetragona</i> , a Circumpolar Evergreen Shrub, to Experimentally Delayed Snowmelt
Bibliographic record
Abstract
While there has been a general trend of climate warming in the Arctic causing early snowmelt and prolonged growing season, climate change models for some areas in the High Arctic suggest increased snow accumulation, delayed spring melt, and consequently shorter growing season. We tested the vegetative and reproductive responses of Cassiope tetragona, an arctic shrub, to increased snow depth and delayed snowmelt using a snow fence experiment in Adventdalen, Norway (78°54′N, 18°01′E). We recorded seasonal shoot length, number of leaves, and capsules per shoot for three summers (2005–2007): two prior and one after the treatment began. Phenological events were recorded in 2007. The number of seeds per capsule was counted and seed germination was tested. Phenological development was significantly delayed behind fences, with lower shoot length, number of leaves, capsules, and seeds per shoot segment (16, 20, 54 and 11%, respectively) compared to controls, but there was no difference in these parameters between plots prior to fence establishment. Seed viability was unaffected by treatment. A delay in start of growing season due to delayed snowmelt decreased vegetative and reproductive performance of C. tetragona; earlier melt may therefore improve performance. This may lead to floral composition change in some high arctic locations.
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 | 0.001 |
| 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".