Acetic Acid Fumigation of Fruit Storage Bins to Control Diapausing Codling Moth Larvae
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Bibliographic record
Abstract
Fumigation with glacial acetic acid (AA) vapor successfully kills post-harvest pathogens on tree fruits and berries and reduces their spoilage in storage. In this study, we investigated whether a similar approach could be implemented to eradicate diapausing larvae of the codling moth, Cydia pomonella (L.), from fruit harvest bins they commonly infest. In 24-h tests conducted in 0.023-m 3 fumigation chambers using two concentrations of vaporized AA [117,360 and 174,823 cumulative parts per million-hours (ppm-h)], mortality of diapausing larvae was 81% and 100%, respectively. A similar 24-h exposure to a 61,940 cumulative ppm-h treatment of AA caused no mortality. A 24-h fumigation of diapausing codling moth larvae placed in scaled-down plastic fruit bins treated with 55 mL of AA evaporated into a 1-m 3 chamber caused 100% mortality. The same fumigation treatment of artificially infested, scaled-down wooden fruit bins caused no significant mortality of test larvae. Atmospheric concentrations of AA vapor in 1-m 3 fumigation chambers containing wooden bins could not be maintained at levels necessary to cause insect death, even after multiple injections of AA. We hypothesize that either the wood or the moisture contained therein absorbs or in some other way interacts with the AA vapor. The use of AA as a fumigant targeting codling moth larvae in wooden bins is not practical or economical at this time. Fumigation of plastic fruit bins with AA would provide an economical and environmentally friendly method of killing diapausing codling moth larvae. The successful disinfestations of plastic fruit bins of codling moth would prevent these bins from being an external source of infestation, thereby decreasing overall codling moth infestation in orchards, which in turn benefits current density-dependent management practices used for the area-wide control of codling moth. chemical name: Glacial Acetic Acid (AA).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it