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Record W2885984322 · doi:10.1038/s41598-018-30167-5

Immuno-impedimetric Biosensor for Onsite Monitoring of Ascospores and Forecasting of Sclerotinia Stem Rot of Canola

2018· article· en· W2885984322 on OpenAlexafffund
Lian C. T. Shoute, Afreen Anwar, Scott MacKay, Gaser N. Abdelrasoul, Donghai Lin, Zhimin Yan, Nguyễn Thị Vân Anh, Mark T. McDermott, Manzoor A. Shah, Jian Yang, Jie Chen, Xiujie S. Li

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersAlberta Canola Producers CommissionInnotech AlbertaUniversity of AlbertaAlberta Crop Industry Development Fund
KeywordsSclerotinia sclerotiorumSclerotiniaStem rotCanolaBiologyAscocarpAgronomyBotany

Abstract

fetched live from OpenAlex

Sclerotinia stem rot, caused by the fungal pathogen Sclerotinia sclerotiorum, is a destructive disease of canola and many other broadleaf crops. The primary inoculum responsible for initiating Sclerotinia epidemics is airborne ascospores released from the apothecia of sclerotia. Timely detection of the presence of airborne ascospores can serve as an early-warning system for forecasting and management of the disease. A major challenge is to develop a portable and automated device which can be deployed onsite to detect and quantify the presence of minute quantities of ascospores in the air and serves as a unit in a network of systems for forecasting of the epidemic. In this communication, we present the development of an impedimetric non-Faradaic biosensor based on anti-S. sclerotiorum polyclonal antibodies as probes to selectively capture the ascospores and sense their binding by an impedance based interdigitated electrode which was found to directly and unambiguously correlate the number of ascospores on sensor surface with the impedance response.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.156

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.225
Teacher spread0.183 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations26
Published2018
Admission routes2
Has abstractyes

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