Effect of Herbicide Management Practices Used by Invasive Plant Managers on<i>Berteroa incana</i>(Hoary Alyssum) Seed Biology and Control
Bibliographic record
Abstract
Abstract Hoary alyssum [Berteroa incana(L.) DC.] is a nonnative invasive forb that is noxious in California, Idaho, Michigan, Montana, Oregon, Washington, Wyoming, Alberta, British Columbia, and Saskatchewan. ManagingB. incanais difficult, because it has an extended flowering period, during which plants simultaneously flower and produce seeds. Consequently, poorly timed herbicide applications may killB. incanaflowers but not prevent viable seed production. We examined how different herbicide management practices used by invasive plant managers affectedB. incanaseed production and viability the year of application as well as population density 1 yr after application. Professional invasive plant managers sprayedB. incanawith various herbicides as part of their current management practices at six sites in southwestern Montana in summer 2016. We collectedB. incanaplants at 4 wk postapplication for seed biology analyses. Across the six sites, nonsprayedB. incanaproduced 5 to 1,855 seeds plant−1and averaged 429 seeds plant−1. Seed production was reduced by 64% to 99% with 7 of the 11 herbicide applications.Berteroa incanaseed viability in nonsprayed areas averaged 53% and ranged from 36% to 73% across the sites. Nine of the 10 herbicide applications used by invasive plant managers reduced seed viability 49% to 100%. Few of the herbicide management practices reducedB. incana’s population density the following growing season, suggesting that managers should expect reoccurring infestations at least 1 yr after application. Our results show that invasive plant managers can reduceB. incanaviable seed production even when spraying plants that have flowered and formed seed pods. However, sites may need to be monitored for additional years to treat reoccurring infestations.
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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".