Ham Radio 360: Field Day 2016 w/ KX9X-ARRL and the Gang
Bibliographic record
Abstract
Field Day 2016 is upon us! Every year Amateurs from all over These United States and Canada spend the 4th weekend of June operating their radios in an effort to hone the craft! Sean, KX9X, Media and Public Relations Manager for the ARRL comes by to share the Leagues Perspective of the event as well as some tips and tricks to make this Field Day one to remember. Following Sean; Jeremy and George come in to discuss the plans they are making for FD2016! Jeremy has a secret, George is planning on playing with Bay-Net and I will be operating with my local club as well. @ARRL_PR on Twitter ARRL Field Day Home page ARRL Field Day Locator Bay-Net FD2014 KF7IJZ FD2014 *yep, in the intro-I said it wrong, it's the American Radio Relay League (not amateur). I'm sure it has noting to do with my dyslexia* your transparent host Cale KX2 News: Nick, N3WG, a former guest on the show, and friend from the Bay-Net Group recently posted a Pignology update for all you KX2 Owners: Find it Here on Youtube Don't Miss It: Make Sure You Listen all they way to the end, and then some! We feature Adam Curry, the Podfather, and his No Agenda Podcast #831 speaking about the upcoming Field Day. Podcast Featured: This week Dave Jackson, my Podcast Mentor, from the School of Podcasting shared my story of you (the listener) sending me to Dayton Hamvention! You can find the show Here! KB1HQS in Print! Click for more info
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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 | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.812 | 0.585 |
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; the direct Gemma label and the distilled Codex classifier 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".