M<scp>ANAGEMENT OF</scp> A<scp>GRICULTURAL</scp> I<scp>NSECTS WITH</scp> P<scp>HYSICAL</scp> C<scp>ONTROL</scp> M<scp>ETHODS</scp>
Bibliographic record
Abstract
Ideally, integrated pest management should rely on an array of tactics. In reality, the main technologies in use are synthetic pesticides. Because of well-documented problems with reliance on synthetic pesticides, viable alternatives are sorely needed. Physical controls can be classified as passive (e.g., trenches, fences, organic mulch, particle films, inert dusts, and oils), active (e.g., mechanical, polishing, pneumatic, impact, and thermal), and miscellaneous (e.g., cold storage, heated air, flaming, hot-water immersion). Some physical methods such as oils have been used successfully for preharvest treatments for decades. Another recently developed method for preharvest situations is particle films. As we move from production to the consumer, legal constraints restrict the number of options available. Consequently, several physical control methods are used in postharvest situations. Two noteworthy examples are the entoleter, an impacting machine used to crush all insect stages in flour, and hot-water immersion of mangoes, used to kill tephritid fruit fly immatures in fruit. The future of physical control methods will be influenced by sociolegal issues and by new developments in basic and applied research.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.028 |
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".