Mass spectrometry as a catalyst for inclusive design practices
Bibliographic record
Abstract
Amid introductory training in the use of a liquid microjunction-surface sampling probe (LMJ-SSP) for ambient ionization mass spectrometry (AIMS) and associated processes such as sample preparation, the inaccessibility of the analytical chemistry laboratory space for persons with physical disabilities became evident. Guided by personal experiences living with tremors, a constant involuntary oscillation of the hands, a series of low-cost 3D-printed adaptive tools were developed as aids for students and researchers with disabilities affecting the fine motor skills. The designs concentrate on routine sample preparation practices required for AIMS methods, including solid sample preparation, liquid sample preparation into small vials, and the opening of small vials. Grips and guides for solid sample scooping tools, modified vial holders that accommodate tremors of the hands, and openers for small vials were designed. Open-source CAD platforms were used exclusively to limit financial barriers to inclusive practice. Once developed, these tools were applied in experimental procedures using the LMJ-SSP, and a substantial increase in self-efficacy was experienced. The purpose of this work was to cultivate tangible, meaningful change within the chemistry laboratory space to further democratize access to analytical methods, and to promote the value of inclusive practices.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".