Bibliographic record
Abstract
The past few decades have seen a promising trend in point-of-care diagnostics, with microfluidic technologies at the cornerstone of this emerging field. Microfluidic devices are platforms the size of a microscope slide, or smaller, that are comprised of various circuits connected by miniature tubing systems. These technologies can be coupled to many common analytical detection techniques and promise rapid simultaneous analyses and automatic reporting, while utilizing minute volumes of samples and reagents (1). Microfluidic chips are typically made out of a transparent polymer, polydimethylsiloxane, through a process called photolithography. A silicon plate is covered with a printed “photomask.” With exposure to UV light, the pattern on the photomask is transferred to a light-sensitive chemical “photoresist” on the silicon substrate. The photoresist resists subsequent chemical treatments, allowing an inverse image of the photomask pattern to be engraved into the silicon. This engraved image serves as a mold for the chip. Polydimethylsiloxane is poured over the mold and hardened to create the final chip. More recently, 3D printing has been employed to create microfluidic chips, which has greatly simplified the process of creating 3D chips. A 3D printer builds the shape of the circuits with plastic and is then submerged in polydimethylsiloxane to produce the chip.
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 | 0.022 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".