Bibliographic record
Abstract
At the beginning of April 2015, two Dutch polar researchers and explorers, Marc Cornelissen and Philip de Roo, set off on crosscountry skis to investigate an area of thinning ice in northern Canada known as the Last Ice Area.Toward the end of the day on April 27, Cornelissen left a cheerful voicemail back home saying that he and his companion were having to ski in their underwear because of unexpectedly warm conditions.He also said that they might have to take a detour to get to their ultimate destination, Bathurst Island, because of unexpectedly thin ice.That would be the last such message.The next day the Royal Canadian Mounted Police received an emergency message from the two-man team, and when a pilot surveyed the area, he spotted the pair's sled dog but not the explorers.One corpse subsequently was recovered.That same month, coincidentally, Harper's Magazine published an article, "Rotting Ice," by the intrepid nature writer and naturalist Gretel Ehrlich, describing repeated visits she had paid to seal-hunting Inuit in Greenland's far north, people living in some of the world's most remote villages.Because of ever widening open waters and ever scarcer ice fl oes capable of supporting seals, the native men and women were x Preface
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.469 | 0.284 |
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".