Bibliographic record
Abstract
The muskrat (Ondatra zibethicus) is a common, semi-aquatic rodent native to the United States (Figure 1). It spends its life in aquatic habitats and is well adapted for swimming.\nAlthough muskrats are an important part of native ecosystems, their burrowing and foraging activities can damage agricultural crops, native marshes and water control systems, such as aquaculture and farm ponds and levees. Such damage can significantly impact agricultural crops like rice that rely on consistent water levels for growth.\nMuskrats also cause damage by eating agricultural crops, other vegetation, and crayfish, mussels and other aquaculture products. Loss of vegetation from muskrat foraging can impact marsh viability and habitats for other species, including waterfowl. Habitat restoration often takes years, negatively impacting fish and wildlife.\nEconomic losses due to muskrat damage in Arkansas, California, Louisiana and Mississippi likely exceed most other states combined, primarily because of the vast amounts of productive marshlands and types of crops (i.e., rice, fish, crayfish and vegetable crops) grown in those states.\nThe 16 subspecies of Ondatra muskrats in North America are widely distributed (Figure 6). They are found from northern Mexico to northern Alaska, and most of northern Canada. The round-tailed muskrat is found primarily in Florida and parts of southern Georgia. Muskrats are not commonly found in dryer, desert type habitats.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.036 | 0.044 |
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; both teacher heads 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".