Weed Competition in a Mature Matted Row Strawberry Planting
Bibliographic record
Abstract
Various levels of weed competition were implemented in a second-year well-established strawberry ( Fragaria × ananassa `Jewel') planting by cultivating and hand weed removal for defined periods of time over 3 years. The impact of weeds on subsequent productivity was then determined. Sixteen treatments were established where weeds were allowed to grow for defined periods (0, 1, 2, 3, 4, or 5 months) throughout the growing season. Treatments were maintained in the plots for 3 consecutive years. Spring weed biomass in 1997 had no impact on yield that same year. Weed biomass in 1997 was negatively associated with yield in 1998, although the trend was nonsignificant. However, several individual contrasts were significant. For example, the weed-free control treatment had the highest average yield, while season-long weed competition reduced yield by 14%. The inverse relationship between weed biomass and fruit yield became significant in 1999. For every 100 g·m -2 increase in weed biomass in 1998, fruit yield was reduced by 6% in 1999. Season-long uncontrolled weed growth reduced productivity by 51%. However, several plots with a limited amount of weed competition had higher yields than the continuously weeded control. These data indicate that yields from a well-established strawberry planting may not be vulnerable to a limited amount of weed competition for at least 2 years. Furthermore, data suggest that hand weeding and cultivation on a monthly basis for multiple years may be damaging as well. Growers should direct a majority of their efforts and resources toward controlling weeds in the planting year. Once the planting is well-established, growers may limit the number of times they hand weed to two or three per season.
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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 | 0.000 | 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".