Hands-On Experimentation in the Fluid Mechanics Classroom as Homework With eFluids.com
Bibliographic record
Abstract
In an introductory fluid mechanics course, it is important for students to realize that the mathematical models they are deriving in class sometimes model the real world well and sometimes not so well. One way to demonstrate this is to have the students model a simple experiment and compare the results of the model to those of the experiment. This exercise teaches the importance of the model assumptions and the applicability of the model. It would be even more effective if the experiments were simple enough so that students could do them at home as a homework assignment, rather than restricting their experience to a “canned” two hour lab course. At eFluids.com, we are building a library of such experiments in an effort to build a community of educators that moves beyond the traditional mathematical exercises for homework. Here, we describe a number of these experiments and how they can be used in classes. We also present some methods of using the eFluids.com Gallery of Images in the classroom to give students the opportunity to see “Fluids in Action.” Finally, we introduce the eFluids Olympiad section where faculty can post effective and “interesting” homework problems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".