Recent Mammals of Alaska S. O. MacDonald , J. A. Cook . 2009. Recent Mammals of Alaska. University of Alaska Press. Fairbanks, Alaska. 387 pp. ISBN: 978-1-60223-047-7. price (hardbound: alkaline paper), $55.00.
Bibliographic record
Abstract
Alaska is more than twice the size of Texas and has more than 10,000 km of coastline (the distance from Laramie, Wyoming, to Buenos Aires, Argentina). It is not only a vast land but also a difficult one in which to work during much of the year. Notable mammalogists, from C. Hart Merriam and Joseph Grinnell—the founders of mammalogy—to the authors and their students (many of whom are pictured in this book), have worked diligently to clarify the fauna of this northernmost state, a last redoubt for much of the American megafauna. The book begins with a nice overview of the history of Alaska's mammal collectors and scientific expeditions. These were heroic efforts in the early days of exploration. On an early expedition Major Robert Kennicott died of heart failure while exploring the Yukon in 1866 (the Kennicott Glacier, Kennicott Valley, and Kennicott River are named in his honor). Kennicott was replaced by William Healey Dall (who first observed white sheep on Mt. McKinley, a species that would subsequently be named the Dall Sheep, Ovis dalli, when it was formally described by Nelson in 1884). Dall (who was a student of the Harvard anatomist and mammalogist, Jeffries Wyman, the father of forensic anthropology) had been exploring Siberia when he learned of Kennicott's death, and he left his Siberian expedition and moved to the Yukon River to continue the exploration of that area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.103 | 0.050 |
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".