Design, Development, and Validation of an End-User Photo-Thermal Sensing Platform for Rapid Detection and Quantification of Analytes in Fluidic Samples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ability to detect the presence of specific analytes and quantify their titers in fluidic samples is essential in many industries, spanning from food industries to law enforcement to healthcare and beyond. The existing technologies used for this purpose require the use of specialty equipment by trained professionals in a laboratory setting to function (e.g., mass spectrometry or ELISA) which greatly increases the cost and time taken to receive actionable results. Portable and inexpensive tests exist – Lateral Flow Immunoassays – however these tests are only qualitative and frequently have an inferior limit of detection. To date, several sensing devices have been designed to interrogate these LFIAs and decrease their limit of detection, however, these devices are often prohibitively expensive. This thesis outlines attempts to design and validate a sensing platform which could inexpensively enhance the limit of detection of LFIAs.The prototype is then validated through both lab-based and human experiments.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it