NASA Solar Eclipse Trainings by NASA HEAT
Bibliographic record
Abstract
Resource Description: For the Annular Solar Eclipse in October 2023, NASA HEAT prepared a training course on SATERN (an internal learning management system) for NASA colleagues. There were four parts in the course: · Part 1: Welcome · Part 2: Science, Safety, and Engagement · Part 3: NASA Eclipse Science · Part 4: Engaging the Public in Eclipse Activities Before the April 8, 2024 total solar eclipse that would pass over Mexico, the United States, and Canada, NASA HEAT prepared another training course on SATERN for NASA colleagues. The training gave all personnel who were engaging with the public for the total eclipse the skills they needed to represent NASA. There were three parts to the curriculum: Part 1-2: Eclipse Essentials Basics of a total eclipse. That training was required for all those who would be engaging with the public for the eclipse and would discuss safety and NASA key messages. Part 3: NASA Science during the eclipse. That training was optional, but highly recommended. It covered what NASA learned from an eclipse and how NASA understood the Sun-Earth relationship. Part 4: Participating in eclipse activities. That training was optional, but also highly recommended. It described NASA’s local and national partners for engaging in the eclipse and descriptions of hands-on activities one could do. After the training, participants were invited to download all the course materials on the public NASA eclipse website for their own use. Project Description: NASA Heliophysics Education Activation Team (NASA HEAT) engages communities across the nation with educational programs about heliophysics. As part of the NASA Science Activation program (NNH21ZDA001N-SCIACT), NASA HEAT actively partners with scientists, educators, and communicators to provide understandable science educational content and experiences to people of all ages and backgrounds.NASA HEAT connects audiences across the nation to the innovative and captivating science of heliophysics. From nationwide educational programming (including the 2023 and 2024 solar eclipses) to community-centric projects, NASA HEAT is actively partnering with museums, youth organizations, classroom teachers, and many others to teach people about our closest star, the Sun, and how it affects our lives. Individual Citation: NASA Heliophysics Education Activation Team (NASA HEAT), part of NASA's Science Activation portfolio, NNH21ZDA001N-SCIACT, Sasser, L., Milotte, C., Ng, C., Reed S., Odenwald, S., Kirk, M., Davis, H., Hunt Estevez, C., Fischer, H. NASA Solar Eclipse Trainings. (2025) [presentation; other]. Zenodo. {10.5281/zenodo.16423641} Collected/Created by NASA HEAT as part of NASA's Science Activation Program, NNH21ZDA001N-SCIACT. Community Citation: NASA SciAct Eclipse 2023-2024, Zenodo. https://zenodo.org/communities/sciact_eclipse/
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.451 | 0.213 |
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".