Controlling Purple Jewelweed (<i>Impatiens glandulifera</i>): Assessment of Feasibility and Costs
Bibliographic record
Abstract
We experimentally tested the feasibility of a control campaign of purple jewelweed (Impatiens glandulifera), an exotic invasive species in Europe and North America. We evaluated the amount of time and money required to control the plant along riverbanks, with particular attention paid to the recovery of riparian vegetation following hand pulling and bagging. Work time was directly and significantly related to stem density and fresh biomass of the invader, but the relationship was stronger for density. Density and biomass were strongly reduced by the first hand-pulling operation from a mean of 45 to 2 stems m−2 and from a mean of 0.95 kg m−2 to nearly zero, a good performance but not enough to negate the need for a second hand pulling later in the summer. A single hand pulling significantly reduced the cover of purple jewelweed from to 30% to 7%. Riparian vegetation disturbed by the first hand pulling largely recovered during the following 30 d. Expressed over an area of 1 ha, the total amount of time required to control purple jewelweed is 1,400 work hours over 2 yr, or a minimum investment of Can$21,000 (US$17,000). Although controlling a well-established purple jewelweed population is expensive, to properly evaluate the benefits, we must also consider the costs of soil erosion caused by this species.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".