Bibliographic record
Abstract
Niche differentiation is a way in which similar species avoid competition. Some species do this by specializing in certain prey items. This review aims to determine why the dire wolf (Canis dirus) went extinct while its similar and less abundant relative, the grey wolf (Canis lupus) did not. Both species were present in North America during the Pleistocene, though only one went extinct during the Quaternary extinction event. Physiological differences existed between the two species, mostly due to a greater focus in hypercarnivory for dire wolves. Dire wolves had more robust frame and skull, greater bite strength, and larger carnasials and canines. These differences in dire wolf morphology all help it to handle and kill larger prey species, while the more lithe grey wolf is better adapted to switching to smaller alternative prey. Dire wolves at have been shown to consume mostly large herbivores while grey wolves can survive with lagomorphs as a primary food source. Larger carnivore body size means reduction in locomotor performance, which means that when many mega-herbivores went extinct at the end of the Pleistocene, dire wolves were not as well adapted to switch to smaller prey as grey wolves are. Their naturally larger body mass also means that they needed higher caloric input to maintain their body condition and fecundity. Overall, Canis dirus specialized in larger prey than Canis lupus, so when this prey became extinct, the dire wolf went extinct along with other hypercarnivores such as the North American lion, Smilodon, and short-faced bear.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".