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Record W6948387935 · doi:10.5061/dryad.sxksn03fk

Enhanced insecticidal activity of isoparaffin by ozone as an adjuvant

2025· dataset· en· W6948387935 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2025
Typedataset
Languageen
FieldMedicine
TopicFungal Biology and Applications
Canadian institutionsTeck (Canada)
Fundersnot available
KeywordsInsectPesticideOzoneAdjuvantCockroach

Abstract

fetched live from OpenAlex

Recently, concerns have been raised regarding control methods for domesticated sanitary pests. For many years, we have relied on chemical pesticides for control, but their negative effects on the human body and increased insecticide resistance have been reported, highlighting the necessity to develop new control technologies. Therefore, we focused on insect respiratory systems, for which it is considered difficult to develop insecticide resistance. In this study, the synergistic effects of isoparaffin and ozone were tested. The insect mortality rates (%) were not significantly different, reaching almost 100 percent when sprayed with SS (spray substance) and SS+O3 (SS containing Ozone). However, death times (minutes) were noticeably different. In cockroach P. fuliginosa, the death times were 10.5 ± 2.6 min. and 1.6 ± 0.5 min. (mean ± SD) for SS and SS+O3, respectively. The Asian tiger mosquito A. albopictus exhibited a death time of 13.5 ± 20.7 min from SS and 1.6 ± 1.4 min from SS+O3. Insects sprayed with SS+O3 died significantly faster, at speeds up to 6 times those observed in SS treatment alone. SEM observation indicated death was caused when the spiracles of the insects were covered with isoparaffin degraded by ozone, resulting in suffocation. From these results, we conclude the combination of isoparaffin and ozone offers a promising new insecticide against pest insects.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.316
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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