Single ascospore detection for the forecasting of <i>Sclerotinia</i> stem rot of canola
Bibliographic record
Abstract
Smart-agriculture technologies comprise a set of management systems designed to sustainably increase the efficiency and productivity of farming. In this paper, we present a lab-on-a-chip device that can be employed as a plant disease forecasting tool for canola crop. Our device can be employed as a platform to forecast potential outbreaks of one of the most devastating diseases of canola and other crops, Sclerotinia stem rot. The system consists of a microfluidic chip capable of detecting single airborne Sclerotinia sclerotiorum ascospores. Target ascospores are injected into the chip and selectively captured by dielectrophoresis, while other spores in the sample are flushed away. Afterward, captured ascospores are released into the flow stream of the channel and are detected employing electrochemical impedance spectroscopy and coplanar microelectrodes. Our device provides a design for a low-cost, miniaturized, and automated platform technology for airborne spore detection and disease prevention.
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".