Demography of <i>Rumex acetosella</i> in lowbush blueberry (<i>Vaccinium angustifolium</i>)
Bibliographic record
Abstract
Summary The demography of Rumex acetosella ramets and seedlings was monitored within and between blueberry clones over the two‐year lowbush blueberry production cycle in Nova Scotia, Canada. Overwintering ramets constituted the majority (>70%) of the flowering ramet population. A small proportion of ramets emerging in May and June flowered, but no ramets emerging between July and November flowered in the year of emergence. Emergence of new ramets was season‐long and ramet populations were regulated by a cycle of birth and death in each production year. Ramet populations in blueberry patches had higher growth rates and lower mortality than ramet populations in bare soil patches in the non‐bearing year, resulting in large net gains to ramet populations in blueberry patches during this production year. Ramet population growth rates and mortality were similar in blueberry and bare soil patches in the bearing year and net ramet populations declined during this production year. Survival rates of overwintering and new ramets varied, but ramets from both the overwintering and monthly cohorts contributed to a distinct ramet age structure at the end of each season. Seedling survival ranged from 6 ± 6 to 51 ± 12% across sites, and no seedlings flowered in the year of emergence. Transition probabilities of ramets and seedlings were used to develop a life‐cycle model of R. acetosella for the 2‐year lowbush blueberry production cycle. This model has utility in developing new management strategies for R. acetosella in lowbush blueberry.
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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 | 0.000 |
| 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".