Efficacy of varying rates of herbicide and surfactant for the control of understory oriental bittersweet (Celastrus orbiculatus Thunb.) plants in an Appalachian hardwood forest
Bibliographic record
Abstract
Oriental bittersweet (Celastrus orbiculatus) is an invasive climbing, twining vine that can grow up into the forest canopy effectively inhibiting growth and light exposure on affected trees. A local landowner who had treated bittersweet with various rates of a glyphosate-based herbicide claimed that higher than recommended rates of herbicide were needed to effectively control the invasive plant. This study was established to assess the validity of this claim and to explore the interaction of glyphosate and surfactant effects on the efficacy of bittersweet control. The goal was to determine an ideal treatment of herbicide and surfactant rates for the effective chemical control of C. orbiculatus. Four rates of glyphosate herbicide in the form of Accord ConcentrateRTM (0%, 2.5%, 5%, and 10 % volume to volume) were crossed with four rates of a common surfactant (Cide-Kick IIRTM; 0%, 0.5%, 1%, and 2%) to create 16 treatments. Treatments were randomly assigned to individual plants growing in the understory of two forested areas in northern West Virginia. Five replicates for each treatment at each site were separated into discrete blocks to account for any microsite variation that might be present within the treatment area. Apart from the surfactant only treatments, all glyphosate treatments were highly effective in defoliating the bittersweet stems. This paper details the first-year results of the study and provides a glimpse of attributes that occur on this invasive species as a result of herbicide toxicity.
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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 | 0.001 | 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 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".