Bibliographic record
Abstract
Bird species new to the Arctic call across ancient forests where the buzz is from the super sawmills, not the sound of elk hooves. Oil and gas wells plumb the tundra depths, and the pipelines scarify the surface, pumping fossil wealth south with a return flow measured in dollars and rubles. The eternal ice is going and tourist ships are coming, ironically to see the icy landscape that is disappearing. This is the Arctic today. Global change has been a fact of life for many indigenous peoples in remote parts of the world for many decades. In Svalbard, Norway, which lies north of Europe in the Arctic Ocean, January 2006 was warmer than any previous April while April was five standard deviations warmer than average. Over the longer term, the Inuit in Nunavut, Canada, have found that hunting for seals—traditionally their major food supply—has become much more difficult as sea pup numbers have declined along with sea ice [Arctic Climate Impact Assessment, 2004]. About 20 Alaskan villages are candidates for relocation because of severe coastal erosion, exacerbated by loss of protective sea ice. For example, the U.S. Army Corps of Engineers estimates that it could cost from $100 million to $400 million to relocate Kivalina, which has only 385 inhabitants [U.S. General Accounting Office, 2003].
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.004 |
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".