The Klondike Gold Rush and the Dead Horse Trail
Bibliographic record
Abstract
Abstract: The Klondike Gold Rush is known in part for the hardships that men and women faced as they travelled the trails to Dawson City in the Yukon. One of the lesser-known aspects of this event is the tragedy that befell thousands of horses that were killed on the Chilkoot and White Pass Trails. The White pass trail would be given the moniker “The Dead Horse Trail.” With so many men trying to get thousands of pounds of goods over the mountain passes from the Alaskan seacoast towns of Skagway and Dyea, pack animals were crucial for the task. However, most if not all of these horses would not survive. This aspect of the gold rush is something that deeply disturbed many gold seekers, and many recounted the horrors years later in their diaries and manuscripts. My paper will ask the question: what were the causes that led to the demise of so many horses on the journey to find gold, and how did they die? PART OF SESSION 4C. SICKNESS AND DEATH: Comment: Tom Taylor, Seattle University Chair: Alyson Roy, University of Idaho Ben Hecko, University of Portland, undergraduate student“Plague and Progress: An Analysis of Giovanni Boccaccio’s Decameron and Reform during the Initial Outbreak of the Black Death” Anika Esther Martin, Eastern Washington University, undergraduate student“The ‘English Bath’: English Sweating Sickness and the 1529 Continental Outbreak” Patricia A. McManigal, Boise State University, undergraduate student“The Holodomor: The Trickle-Down effect of Political and Economic Choices” Brian O’Riley, Eastern Washington University, graduate student“The Klondike Gold Rush and the Dead Horse Trail”
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".