Bibliographic record
Abstract
Over the course of the summer, I have continued to work within the Escobedo Lab on our various projects involving microfluidics and Raman spectroscopy. Specializing in Lab-On-Chip design, I focused on furthering our research into bacterial biofilm generation and microplastic separation. This included developing a microfluidic chip to enable the interaction between cholera bacteria and intestinal lining and furthering a previous chip design which uses centrifugal force to separate microplastics from a continuously flowing stream of water. I also assisted other researchers with their chip designs, including aiding in the brainstorming of a model meant to digest biological samples to extract microplastics embedded in them, and assisting in designing an entrapment system to concentrate molecules over a SERS (Surface Enhanced Raman Spectrum) based sensor. During the course of my lab work, I also noted some areas in which the lab required additional equipment or features to run more effectively. This led to me developing a number of fixes and aides during our down time between experiments, which I then modeled in CAD and made using our in-lab SLA 3D printers. This solved a variety of issues, including making camera stands to better record experiments and developing covered sample holders for light sensitive materials. Over the course of my summer, I am pleased to say I progressed several areas of our laboratory work, and I am looking forward to continuing my research for my 4th-year thesis this coming year
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".