SUFB 873: Climate Change Heroes Help Inspire Us To Do Better
Bibliographic record
Abstract
There were two stories that I came across this week that I feel should accompany the last episode on how Climate Change is messing up the planet. Listening to a story on Climate Change could be demoralizing, but there are other stories that could provide hope.Those stories are about people who inspire us to do better or help us understand how to do things better. The first story I discuss is about Greta Thunberg as she arrives in New York after sailing across the Atlantic at the age of 16-17 to participate in UN talks on Climate Change and hopefully inspire people in the US to take more action against Climate Change.The second story is about a friend and colleague, Dr. Brett Fevaro, who recently took a road trip with his family in their Tesla to show that people COULD go on road trips and find charging stations.Two Stories I Referred To In This One:1) https://www.cbc.ca/news/canada/newfoundland-labrador/cheap-driving-electric-vehicle-1.5260631?fbclid=IwAR26oLh56gZNyrl1HIAdGCemSCXOkcJwokoWj_OoxaN58yIxKe0RKsMwSJQ2) https://www.theguardian.com/environment/live/2019/aug/28/greta-thunberg-sails-into-new-york-waters-after-crossing-atlantic-live-news?page=with%3Ablock-5d66def88f08ea59f447ad9eBoth stories provide hope for me. Share your stories of hope in this Climate Change era in our Facebook Group: http://www.speakupforblue.com/group.Want to be more eco-friendly? Buy certified eco-friendly products from our affiliate partner the Grove Collaborative: http://www.speakupforblue.com/goocean.Check out the new Speak Up For The Ocean Blue Podcast App: http://www.speakupforblue.com/app.Speak Up For Blue InstagramSpeak Up For Blue Twitter
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.
How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.018 |
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; both teacher heads 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".