Evaluating acephate and azadirachtin for control of <i>Psyllopsis discrepans</i> (Flor) (Hemiptera: Psyllidae) and prevention of decline of ash trees
Bibliographic record
Abstract
Abstract Over the past 20 years, ash trees (Oleaceae) in parts of the western United States of America and Canada have been subject to infestations with the psyllid Psyllopsis discrepans (Flor) (Hemiptera: Psyllidae). Infested trees show a series of symptoms, including pseudogalls, canopy loss, and in many cases, tree death. This is an expensive problem for urban forests, particularly in the context of emerald ash borer (Coleoptera: Buprestidae) and Dutch elm disease (Ophiostomataceae), which also impact the diversity of urban forests. This paper presents results from a study on the efficacy of two tree-injected insecticides, Orthene® (acephate) and TreeAzin® (azadirachtin). Trees were treated with these insecticides, and egg and adult psyllids were counted. In addition, canopy cover and severity of pseudogalling were visually assessed. Orthene reduced canopy loss and severity and amount of pseudogalling compared to what occurred on control trees; however, there were more eggs on Orthene-treated trees, indicating that any potential benefit was offset by higher egg counts after treatment. Due to the rapid decline of the ash trees, TreeAzin could not be successfully injected.
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 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".