Gotcha! Which fly trap is the best? An introduction to experimental data collection and analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Collecting data from experimental observations is an important component of the scientific process; likewise, the analysis of the data is essential to understanding the observed trends and patterns from any experiment. Allowing students to practice data collection and analysis is valuable to their scientific literacy and capacity. In this paper, we present a fly trap experiment that gives students the opportunity to discover which combinations of baits and trap types yield the best fly traps. Baits and traps can be made from easily available household goods, allowing this experiment to be set up with minimal preparation and at low cost. Students, individually or in small groups, set up their specific trap and bait combinations and the instructor collects them and places them in an area with flies. After a period of time, the instructor returns the traps to students who count the numbers of trapped flies. With these data, students summarize the results and evaluate the success of different combinations of trap type and baits. The experiment requires one session to construct and set-up the traps and a second session to count the flies and analyze and interpret the data. The experiment is designed for undergraduate students and can be modified to fit students’ past experience with experimental design and statistical analysis. Accompanying article about online adaptation of this lesson: <a href="https://doi.org/10.24918/cs.2021.7">Online Adaptation to "Gotcha! Which fly trap is the best? An introduction to experimental data collection and analysis"</a>
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.
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 | 0.001 |
| 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) | 0.001 | 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 it