Bibliographical reference – how to cite this fact sheet: Nummi, P. (2010): NOBANIS – Invasive Alien Species Fact Sheet – Castor canadensis. – From: Online Database of the European Network on Invasive Alien Species – NOBANIS www.nobanis.org, Date of access
Bibliographic record
Abstract
Species identification Castor canadensis is a large rodent with a flattened tail. Weight 16-32 kg, body length up to 120 cm (Jenkins and Busher 1979, Hill 1982). The external appearance of Castor canadensis is very similar to that of the European beaver (C. fiber L.), however, the nasal bones of C. canadensis are shorter and more rounded and its fur more brownish. The two species also differ in chromosome number: C. canadensis, 2N = 40; C. fiber, 2N = 48 (Lavrov 1983, Jenkins and Busher 1979). The two species can also be identified from the secretions of their anal glands with which the animals scent mark the borders of their territories (Rosell and Sun 1999). Native range Castor canadensis occurs throughout North America except for the arctic tundra and southwestern deserts (Jenkins and Busher 1979). Alien distribution History of introduction and geographical spread Canadian beavers were introduced to Finland in 1937 as a part of the program to reintroduce the exterminated European beaver. The introductions were successful in eastern Finland where two
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.025 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.526 | 0.468 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".