Refining field portable technology: quantification of arsenic field test kits
Bibliographic record
Abstract
This thesis investigates issues related to the detection of arsenic from water sources and the analysis performed to quantify its presence. Through the literature it is shown that colourimetric means of analysis are essential, but current techniques suffer quantitatively, generally relying on a technicians’ ability to visually discern colour. An increased level of control, over the Gutzeit method, would serve to drastically improve the existing means of analysis. With reliance being placed on simple field portable technologies, considerable room has been left for misclassifications. Methodology and means of using these commercial products must therefore be tuned, if they are to be globally accepted as a means of quantification. This thesis compared both benchmark and colourimetric technologies, to redefine in-field analysis. Here the goals were to improve both the classification of arsenic concentrations in water and alleviate the need for technical proficiency. A framework was built under ideal conditions, moving away from the qualitative state of existing colourimetric analyses. A combination of cameras and imaging apparati were put to use, in the acquisition of colourimetric indicator images. MATLAB algorithms were applied to quantitatively discern samples with assembled calibration curves.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".