Update on petroleum spray oil for citrus rust mite control
Bibliographic record
Abstract
The evaluation of ExxonMobil, Helena Chemical Company, PetroCanada, and Sun Oil Company petroleum spray oils for control of citrus rust mite is reported. In an experiment with PetroCanada 455 oil, 20 gallons of oil plus 125 gallons of water per acre applied in April, July, and October did not produce leaf or fruit damage, but did control citrus rust mites for 8 months. One 10 or 15 gallons per acre application of Petro- Canada 455 oil compared favorably with Sun Oil Company 455 oil at the same rates. In general, higher rates of oil provided better and/or longer citrus rust mite control. Oil plus 125 gallons of water per acre provided better residual citrus rust mite control compared to the same oil and rate applied in 30 and 50 gallons of water per acre. A 2 gallon rate of Helena Chemical Company oil provided rust mite control for approximately 40 days in two trials. ExxonMobil 455 oil compared favorably with Sun Oil Company 455 oil at 10 gallons per acre with one application in July. Split applications in June and August of 7.5 gallons of ExxonMobil and Sun Oil Company 455 oils in 125 gallons of water per acre provided 111 days (late June-October) of citrus rust mite control. Overall, petroleum oils from ExxonMobil, Helena Chemical Company, PetroCanada, and Sun Oil Company performed well in these tests.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".