Rethinking a Timeless Titration Experimental Setup through Automation and Open-Source Robotic Technology: Making Titration Accessible for Students of All Abilities
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Titration is a common introductory experiment performed across teaching laboratories from high school to university. Yet, its setup remains inaccessible for students with disabilities, denying them the opportunity for experiential learning. Therefore, rethinking such a setup is required to increase laboratory participation for these students. To remove these physical barriers, an automated titration unit based on existing undergraduate titration setups and universal design concepts, coupled with text-to-speech (TTS) capability, is presented. This unit can connect seamlessly via Bluetooth to any mobile platform and takes advantage of the advances in assistive features, such as TTS, on either a tablet or a smartphone. The cost of this unit is between $300 and $500, not including the cost of the smartphone or tablet. However, with the popularity of mobile devices in our society, these devices are becoming highly affordable, and almost every undergraduate is equipped with such a device. Also, with the emphasis on coding literacy, this autotitration setup serves as an excellent example and exercise on design thinking and automation in an undergraduate chemistry lab.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".