Revised distribution of an Alaskan endemic, the Alaska Hare (<i>Lepus othus</i>), with implications for taxonomy, biogeography, and climate change
Bibliographic record
Abstract
The Alaska Hare (Lepus othus Merriam 1900) is the largest lagomorph in North America but remains one of the most poorly studied terrestrial mammals on the continent. Its current distribution is restricted to western Alaska south of the Brooks Range, but historical accounts from north of the Brooks Range (the North Slope) have led to confusion over its past, present, and predicted future distributions. To determine if L. othus occurs or historically occurred on the North Slope, we surveyed museum collections, vetted observational accounts, and produced a spatial distribution model based on the resulting georeferenced records. We located a historic specimen long presumed lost that suggests the occurrence of L. othus on the North Slope as recently as the late 1800s. We also uncovered evidence of L. othus and (or) Mountain Hare (Lepus timidus Linnaeus 1758) on several islands in the Bering Sea, raising the possibility of recurring gene flow between these closely related species across seasonal ice connecting Asia and North America. While our results paint a more complete picture of the current distribution of L. othus, persistent uncertainties surrounding its taxonomic status and potential northward range shift onto lands reserved for oil and gas development call for additional study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".