Quantitative calculation of the influence of the molecular association between terpenoid repellents and CO2 on their repellency against mosquitoes
Bibliographic record
Abstract
This study attempted to demonstrate the molecular association between terpenoid repellents and CO2,and investigated its influence on their repellency against mosquitoes.Association energy was calculated using computational chemistry software:three-dimensional structures of CO2,22 terpenoid repellents and their complexes that are associated with CO2 were built and optimized using Gaussian View and Gaussian 03W,respectively,and the association energy were obtained in Ampac 8.16.Dependence of repellency on the molecular association was studied by quantitative structure-activity relationship method.Significant activity-affecting parameters were screened from structural descriptors of 22 terpenoid repellents and their complexes,as well as descriptors of characteristic fragments of complexes,which were all calculated by Codessa 2.7.10.The quantitative structure-activity relationship(QSAR) model was obtained to analyze the relationship between the structural descriptors and the logarithm value of the corrected repelling time against Aedes albopictus.Association energies between terpenoid repellents and CO2 were thus calculated,and the results showed that the molecular association between them was strong enough to form the complexes.A statistical QSAR model with four parameters and with R2 of 0.9643 was built,and the most significant activity-affecting parameters were COM-WNSA-3 Weighted PNSA(PNSA3*TMSA/1 000) [Zefirov's PC],f-TerCO2-Min e-n attraction for a C-O bond,M-Max 1-electron reactivity index for an O atom,M-Min(0.1) bond order of an H atom,respectively.The results of computational chemistry calculation show that the molecular association between terpenoid repellents and CO2 is present,and the association can affect the repellency greatly.
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 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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".