Bibliographic record
Abstract
Ondatra zibethicus (Linnaeus, 1766) —Common Muskrat Castor zibethicus Linnaeus, 1766 p.79; Type locality- East Canada. Ondatra zibethicus: Won, 1968 p.200; Yoon, 1992 p.76; Han, 1994 p.47; Won & Smith, 1999 p.26; Han, 2004c p.140. Range: The species has a distribution from the lower reaches of Duman River (river on the border between North Korea and China or North Korea and Russia) and adjacent lakes or reservoirs to the northeastern tip of the Korean Peninsula (Fig. 118). Although muskrat farms exist in South Korea, no established wild population exists. One escapee was caught in 2014 during nutria control at Geum River, Cheonju, South Korea (Jo et al. 2017a). Remarks: Muskrats appeared on the Korean Peninsula in 1965 after introduced populations from the Russian Far East had expanded into extreme northeastern Korea. More recently, fur farmers in South Korea have started to import this species in 2005. Due to the high market value of the animal (the price of one live muskrat is $600–1000 USD), breeders rarely lose this expensive animal and only one confirmed escape of a muskrat from a farm occurred in South Korea in 2014. However, there is a high risk that this species is released and invade wetlands in South Korea, similarly to what happened with nutria (Myocastor coypus).
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".