Bibliographic record
Abstract
Description from Publisher: The Arctic is warming faster than any other region on earth. This rapid climate change has had a devastating effect on the region's ecology and, consequently, on the Inuit people who depend on the Arctic ice for their livelihood and culture. Hailed as one of the greatest documentary photographers of our times, Ragnar Axelsson has been recording the changing face of life in the Arctic for some 30 years. First published in 2010 and with an extended second edition in 2013, Last Days of the Arctic presents 160 of his stunning photographs from Canada and Greenland, with superb duotone printing, captions added for the black-and-white photographs and new images. Axelsson's gorgeous photographs show vast glaciers, sleds gliding across ice and houses buried in snow, but they also depict how the Inuit's changing way of life foresees the changes that are on their way to the rest of the world. The first edition of Last Days of the Arctic won critical acclaim, with photo features in The New York Times. Nominated Book of the Year in the Times, it was described as a ‘gift for the eyes, mind and heart’. The book was named one of the best photography books by the Sunday Times and Germany’s Die Welt. Axelsson's documentary of the same name was aired on the BBC and elsewhere, also to acclaim.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.379 | 0.291 |
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".