Data from: Portable device for the detection of colorimetric assays
Bibliographic record
Abstract
AbstractIn this work, a low-cost, portable device is developed to detect colorimetric assays for in-field and point-of-care (POC) analysis. The device can rapidly detect both pH values and nitrite concentrations of 5 different samples, simultaneously. After mixing samples with specific reagents, a Hi-resolution digital camera collects a picture of the sample, and a single-board computer processes the image in real time to identify the hue-saturation-value (HSV) coordinates of the image. An internal light source reduces the effect of any ambient light so the device can accurately determine the corresponding pH values or nitrite concentrations. The device was purposefully designed to be low-cost, yet versatile and the accuracy of the results have been compared to those from a conventional method. The results obtained for pH values have a mean standard deviation of 0.03 and a correlation coefficient (R) of 0.998. The detection of nitrites is between concentrations of 0.4 to 1.6mg/L with a low detection limit of 0.2mg/L and has a mean standard deviation of 0.073 and an R-value of 0.999. The results represent the great potential of the proposed portable device as an excellent analytical tool for POC colorimetric analysis and offer broad accessibility in resource-limited settings.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.048 |
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".