Pre- and post-vernalization ramet removal reduces flowering of red sorrel (<i>Rumex acetosella</i> L.) in wild blueberry (<i>Vaccinium angustifolium</i> Ait.)
Bibliographic record
Abstract
White, S. N., Boyd, N. S., Van Acker, R. C. and Swanton, C. J. 2015. Pre- and post-vernalization ramet removal reduces flowering of red sorrel (Rumex acetosella L.) in wild blueberry (Vaccinium angustifolium Ait.). Can. J. Plant Sci. 95: 549–556. Red sorrel is a common, ramet-producing herbaceous creeping perennial species in wild blueberry fields. Flowering and seed production occur primarily in overwintering ramets in Nova Scotia, Canada, and growers may therefore be able to adopt fairly simple management strategies to reduce or prevent flowering and seed production in this species. The objective of this research was to determine the effect of pre- and post-vernalization ramet removal on red sorrel flowering ramet density under both controlled and field conditions. Under controlled conditions in a growth facility, ramet populations were subject to pre- and post-vernalization clipping treatments to simulate late autumn and early spring ramet removal. Under field conditions, pre- and post-vernalization ramet removal was achieved using postemergence applications of paraquat applied at a rate of 1 100 g a.i. ha −1 in early autumn and spring. Under controlled conditions, pre- and post-vernalization ramet removal reduced the density of flowering ramets by >95% and significantly reduced the frequency of pots with flowering ramets. Autumn and spring applications of paraquat under field conditions significantly reduced the survival of overwintering ramets, resulting in a significant reduction in the density of flowering ramets. Pre- and post-vernalization ramet removal has the potential to be a component of an integrated weed management strategy for red sorrel in wild blueberry.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".