Making Physical Measurements from Student’s Own Images
Bibliographic record
Abstract
Observational astronomy is a quantitative endeavour in which scientists record and analyse data such as images or spectra. Yet, at public events at observatories, we are often asked “where do you look through?” This demonstrates a gap between the astronomer’s work and the public’s perception of astronomers. The survey course for non-science majors at Kwantlen Polytechnic University, Canada, attempts to bridge this gap, in part, by providing opportunities for students to experience their own scientific process of taking data and analysing them for quantitative results. Two lab activities that involve working with images are described here.\nIn the parallax activity, students take photographs of a ball from two different locations. The ball represents a distant star and the two locations represent the Earth’s motion along its orbit. The images are analysed for parallactic shift against background objects, and ultimately, students work out the distance to the “star”.\nLater in the course, students observe the Moon using 8-inch telescopes with webcam adapted cameras to record image frames, which are combined to make a lunar mosaic. Crater and maria sizes are measured from the mosaic in kilometres and compared to sizes of Earth features. Students also work through the logistical steps of telescope time assignment, scheduling and weather.\nBoth lab activities take students out of the laboratory for active engagement and demonstrate the idea that systematic analysis of image data can yield scientific measurements.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.019 |
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".