Colombian Prototype of a Spirometer, From Classroom to Practice
Bibliographic record
Abstract
Design technological tools from basic sciences would be an excellent way for students to learn about Science, Technology, Engineering, and Mathematics (STEM) applications. Nowadays, young people are more connoisseurs of computer tools and gadgets than other generations. They could use those aptitudes with correct stimulation and orientation to generate new knowledge or improve technological applications in developing countries like Colombia. Designing or improving technology starting from basic knowledge and developing cheap technology is essential for the development of a society. We used a case study in this work, due to COVID 19 pandemic, we propose the design and creation of a prototype of a spirometer started from a class activity to put into practice the knowledge acquired in theoretical class and let students observe and implement physics applications. As a result, elemental physics course students achieve testing it on real people and compare it with known results. The students' spirometer prototype was made with inexpensive implements starting with the knowledge learned in the classroom. With it, students tried it on actual patients, letting them get measurable data consistent with known results from the literature. These experiences increased students' interest in science and its applications. This work shows us that applying STEM methodology from basic levels to practical uses could motivate young people to learn and improve their skills in those topics and see science as a way of life.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".