WIP Paper: Engineering Materials Related Courses at the University of Puerto Rico in Mayagüez (UPRM) after Hurricane Fiona Crossed the Island in September 2022
Bibliographic record
Abstract
On September 18, 2022, a Sunday afternoon, hurricane Fiona entered Mayagüez with a tangential speed of 150 mph and dwelled for longer than five hours since she moved with a linear velocity of only about 5 mph.Our campus was totally devastated and there were neither class lectures nor labs for over two weeks.Immediately thereafter, the campus was again closed for a week due to student and worker strikes.The hurricane and strikes seriously hampered all our undergraduate and graduate courses, and particularly those related to materials science and engineering, because such courses are offered in several departments, including mechanical, civil, industrial, chemical, and electrical, as well as in the other departments of the faculty of science.For example, in our mechanical engineering department, we offer a course on Biomaterials, an interdisciplinary approach with the Biology Department.The present paper illustrates how we are handling the obstacles of losing three weeks of all academic activities, including teaching, research, and services, in order to finish the current semester on time Key words and phrases: natural and man-made phenomena, pollution-free materials and processes, sustainable manufacturing
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.128 | 0.018 |
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".