Bibliographic record
Abstract
Among all the large whales on Earth, the most unusual and least studied is the narwhal, the northernmost whale on the planet and the one most threatened by global warming. Narwhals thrive in the fjords and inlets of northern Canada and Greenland. These elusive whales, whose long tusks were the stuff of medieval European myths and Inuit legends, are uniquely adapted to the Arctic ecosystem and are able to dive below thick sheets of ice to depths of up to 1,500 meters in search of their prey-halibut, cod, and squid. Join Todd McLeish as he travels high above the Arctic circle to meet: Teams of scientific researchers studying the narwhal's life cycle and the mysteries of its tusk Inuit storytellers and hunters Animals that share the narwhals' habitat: walruses, polar bears, bowhead and beluga whales, ivory gulls, and two kinds of seals McLeish consults logbooks kept by whalers and explorers and interviews folklorists and historians to tease out the relationship between the real narwhal and the mythical unicorn. In Colorado, he visits climatologists studying changes in the seasonal cycles of the Arctic ice. From a history of the trade in narwhal tusks to descriptions of narwhals' vocalizations as heard through hydrophones, Narwhals reveals the beauty and thrill of the narwhal and its habitat, and the threat it faces from a rapidly changing world. Watch the trailer: http://www.youtube.com/watch?v=gHwaqdKyLCQ&list=UUge4MONgLFncQ1w1C_BnHcw&index=9&feature=plcp
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.280 | 0.113 |
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".