Bibliographic record
Abstract
During the course of the summer, I had the privilege to work with Dr. Carlos Escobedo and his team of researchers on the topics of microfluidics and Lab-On-Chip (LOC) technology. Microfluidics is a branch of fluid dynamics which utilizes small volumes of fluid to perform tests under ideal conditions, and LOC is the process of using microfluidics for experiments in small-scale environments. I participated in two LOC projects, the first for bacterial analysis and the second for microplastic separation. The goal of the first project was to observe if Vibrio Cholerae bacteria was attracted to a variety of chemo attractants. To observe this interaction under microfluidic conditions, we developed a chip which when flooded with chemoattractant trapped a portion in the chip. We could then remove the non-entrapped chemoattractant from the sides and refill the chip with bacteria. Observations of the bacteria’s movement were then taken using a microscope attached to a digital camera. The second project focused on microplastics, which are plastic particulates under 5mm in diameter, our smallest sample being 10μm. Due to their size standard filtering techniques were ineffective at separating them, which led to us using Deterministic Lateral Displacement (DLD). DLD utilizes an array of pillars to create spots of changing pressure for passing particles. The array is designed so larger particles are unaffected while smaller particulates will be redirected and collected separately. To arrange the plastics into their types, a centrifugal design was used. This design functions similarly to a centrifuge, which by rapidly spinning a substance separates out materials based on density. The chip works under a similar theory, but instead uses a spiral channel to force the particles in a repeated circular spinning motion. The particles then exit with differing directionalities based on where they were in the channel, allowing for collection.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.015 |
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".