American Geophysical Union AMA: Hi Reddit, I am Andrew Yau, Editor of Geophysical Research Letters, here to talk about Jupiter and the exciting findings from NASA’s Juno mission. Ask Me Anything!
Bibliographic record
Abstract
I am Andrew Yau, Professor of Physics at University of Calgary, Canada, and Editor of Geophysical Research Letters (GRL), a research journal published by AGU focusing on high-impact scientific advances in all major geoscience disciplines. I am a space scientist. I design satellite instruments such as ion mass spectrometers, and I am interested in the effects of weather in space around the Earth - and other planets. For example, how and why do solar storms cause the heating of the upper atmosphere and its escape into space here on Earth? How about on Venus, Mars, and Jupiter? How does the solar wind produce the aurora, and the associated electrical currents in the ionosphere here on Earth? How about on Jupiter and Saturn, which also have an internal magnetic field? I’ll be back at 12 EDT to answer your questions. Ask Me Anything! The AGU AMA series is conducted by the Sharing Science program. Sharing Science: By scientists, for everyone. More at sharingscience.agu.org. Thanks, everyone, for participating in today’s AMA. It has been great fun – I hope my answers to your questions have provided a glimpse of the exciting scientific discoveries about the largest planet in our Solar System, Jupiter, from the NASA Juno mission. Some of these discoveries were reported in the recent Special Issue of the Geophysical Research Letters (GRL) for Juno. I encourage you to check out the GRL website for these discoveries as well as some even newer ones that are in the pipeline: http://agupubs.onlinelibrary.wiley.com/hub/issue/10.1002/grl.v44.10/ Sorry I didn’t have a chance to field many of the other questions. Have a great day!
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.269 | 0.287 |
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".