Dielectric analysis of Sclerotinia sclerotiorum airborne inoculum by the measurement of dielectrophoretic trapping voltages using a microfluidic platform
Bibliographic record
Abstract
Sclerotinia stem rot, caused by the fungal pathogen Sclerotinia sclerotiorum is a devastating crop disease. Various forecasting systems have been developed to provide information about the risk of outbreaks and thus avoid the heavy financial burden caused by over-spraying fungicides. In this study, we experimentally determine the dielectric properties of Sclerotinia sclerotiorum airborne spores, one of the main agents of infection in stem rot. The dielectric properties of spores are important parameters for the development of forecasting systems based on dielectrophoretic filters as it provides information about the dielectrophoretic response of spores without the need for iterative testing. A microfluidic platform was employed to estimate the dielectric parameters based on a dielectrophoretic method and in media of different conductivities. In addition, using the multi-shell model, spores were modeled using a realistic ellipsoidal double-shell model. To validate the methodology and analysis, the dielectric properties of human embryonic kidney 293 were also determined and compared to values reported in the literature. The obtained values for the electrical permittivity and conductivity of the spore interior and membrane were found to be insensitive to the conductivity of the external medium. On the other hand, the dielectric parameters of the outer most layer showed a small variation with the conductivity of the external medium. This study represents the first report on the dielectric properties of Sclerotinia sclerotiorum airborne inoculum, and it aims to contribute to the development of forecasting systems based on dielectrophoretic filters.
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 | 0.000 | 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 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".