Bibliographic record
Abstract
An icy stare P olar latitudes have among the harshest of climates on earth, and those ill prepared for these extremes sometimes pay with their lives.Witness those who were bent on finding the northwest passage in the early 1800s.Explorers such as Parry and Franklin, among others, lost ships, supplies, men, and in some cases their own lives in their quest for a route to the orient.They simply weren't well enough prepared.Surprisingly, some vertebrates prepare for such extreme conditions in a manner many would consider wholly inconsistent with life.These creatures allow up to 65% of their body water, including their eyes, to freeze over winter!Rana sylvatica, the North American wood frog, pictured on the front cover, is found across most of the northern tier of North America.While the range of this frog does extend south of Canada into the northeast United States and even further south into northern Georgia, its stronghold is the Canadian shield, extending northwest to include Alaska.It is the only frog found north of the Arctic Circle.This ectothermic anuran survives there-a feat that is nothing short of extraordinary.Winter comes early north of 66 ˚and
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.507 | 0.267 |
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".