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Record W4396515329 · doi:10.22215/etd/2023-15934

A Portable, Rapid Isothermal Nucleic Acid Amplification System with Integrated Microfluidics for Pandemic Surveillance

2023· dissertation· en· W4396515329 on OpenAlexafffund
Jake Staples

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsCarleton UniversityQueen's University
FundersHORIZON EUROPE Framework ProgrammeQueen's UniversityEuropean Commission
KeywordsLoop-mediated isothermal amplificationMicrofluidicsFootprintDNA extractionSIGNAL (programming language)Nucleic acidComputer scienceReal-time computingComputer hardwareMaterials scienceElectronic engineeringNanotechnologyDNAEngineeringPolymerase chain reactionChemistryBiology

Abstract

fetched live from OpenAlex

Integration of small-footprint cost-effective isothermal rapid DNA extraction, ampliőcation, and detection systems is crucial to achieving a point-of-care pathogen detection system suitable for resource-limited locations.The herein system measures low-level ŕuorescent signals in real-time during nucleic acid ampliőcation, while maintaining the desired assay temperature on a low-power, portable footprint.A thermally stable microŕuidic device was implemented to mitigate thermocapillary effects and facilitate optical alignment for automated image capture and signal analysis.I would like to thank Professor Ravi Prakash for his invaluable advice, help, and support as a supervisor throughout my graduate studies.The work achieved would not have been possible without his vision and expertise in the vast areas of science and engineering.I am extremely grateful to have been able to work on this high-impact project that has given me the experience to continue my career in the healthcare technology őeld.I want to thank all of my colleagues at the Organic Sensors

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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 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

Citations0
Published2023
Admission routes2
Has abstractyes

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