Bibliographic record
Abstract
Abstract Teaching Fluid Mechanics and Mass transport to BiologistsThe field of biotechnology is emerging as a mature disciple that calls for a very intimatepartnership between chemical engineers and biologists. Thus several biologists in thebiotechnology profession have a need to learn the basics of chemical engineering in order tooperate effectively in an integrated, cross-disciplinary environment. Since the traditional fluidmechanics and mass transport courses are specifically designed for chemical engineeringstudents with a precise set of prerequisites, it becomes necessary to develop a course speciallytailored for biologists, outside of the usual chemical engineering curriculum.The challenge: There are two major pedagogical challenges in developing an engineering coursefor biologists: (i) the difference in the way engineers and biologists learn – engineers tend tolearn through quantitative and analytical methods while biologists are more comfortable withdescriptive and illustrative learning, and (ii) the non-existence of the usual perquisites amongbiologists. Another critical challenge is the general unavailability of instructional materials toteach fluid mechanics and mass transport to non-engineers.The strategy: This paper will share some unique strategies and experiences in developing anddelivering a fluid mechanics and mass transport course for biologists. The strategies include: (i) Methods to provide the required mathematics background (trigonometry and calculus). (ii) Approaches to make fluid mechanics and mass transport instruction more analysis- based and less quantitative-based. (iii) Project-based learning methods to deliver concepts in fluid mechanics and mass transport. (iv) Use of biology examples to teach chemical engineering concepts (eg., oxygen transfer in the alveoli to explain film theory). (v) Exposure to CHE unit operations through lab tours and field trips.Assessment: It is obvious that the traditional assessments methods and criteria cannot be used foreither assessing this course or the students taking it. However, some of the ABET “a to k”program outcomes were found to be relevant and were applied for evaluation this course. A fewassessments methods adapted from biology education programs will be discussed. This paperwill also discuss student evaluation methods that were specifically developed to test non-chemical engineers. Future plans to include virtual lab modules will also be discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".