SUFB 975: Which Pacific Salmon Species Will Adapt Well To Climate Change?
Bibliographic record
Abstract
Pacific Salmon species love cool, clean waters and they are able to enjoy those waters all throughout British Columbia, Canada. Glacial retreating due to warmer air temperatures has revealed many suitable habitats for Pacific Salmon species; however, those glaciers are getting smaller and some have stopped revealing suitable habitat. It's all due to Climate Change that increased the speed of melting. Less habitat is a bit of a concern to researchers.Some Pacific Salmon species will not do as well in warmer conditions. Listen to the episode to find out which species will do well and which ones won't.Link To Article: https://vancouversun.com/news/local-news/glacial-retreat-good-news-and-bad-news-for-pacific-salmon-speciesDo you think new suitable habitat revealed by retreating glaciers should be protected from humans? Share your thoughts in the Speak Up For Blue Facebook Group: http://www.speakupforblue.com/group.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 TwitterCheck out the Shows on the Speak Up For Blue Network:Marine Conservation Happy Hour Apple Podcasts: https://apple.co/2k4ZB3x Spotify: https://spoti.fi/2kkEElkConCiencia Azul: Apple Podcasts: https://apple.co/2k6XPio Spotify: https://spoti.fi/2k4ZMMfDugongs & Seadragons: Apple Podcasts: https://apple.co/2lB9Blv Spotify: https://spoti.fi/2lV6THtEnvironmental Studies & Sciences Apple Podcasts: https://apple.co/2lx86oh Spotify: https://spoti.fi/2lG8LUhMarine Mammal Science: Apple Podcasts: https://apple.co/2k5pTCI Spotify: https://spoti.fi/2k1YyRLProjects For Wildlife Podcast: Apple Podcasts: https://apple.co/2Oc17gy Spotify: https://spoti.fi/37rinWz
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.003 |
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".