D3.4 - Development of a Lab-on-a-Chip for Detection of Bacteria in Food Samples
Bibliographic record
Abstract
We developed an optical biosensor of nanodimensions, for the detection of bacteria in food samples, which has an integrated Mach-Zehnder Interferometer (MZI) configuration.MZI works on the principle of total internal reflection having a sensor arm, where the bacteria is to be bound by biofunctionalization, and a reference arm.The MZI is fabricated on a silicon substrate with silicon nitride (Refractive index=2.00)acting as the core and silicon dioxide (Refractive index=1.46)acting as the upper and lower cladding.Light from a laser source (He-Ne) will be coupled into the waveguide having the MZI configuration which is split into the two arms, then after a certain distance, they recombine again.This is done by using a diverging and converging Y-junction, respectively.The design of the MZI has been created using simulations in order to get a monomodal propagation of light with minimal losses.The Y-junctions have been designed so as to allow the divergence and convergence of the propagating light with a 3dB split ratio.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".