A molecular approach to identifying the relationship between resource use and availability in Eurasian otters (<i>Lutra</i><i> lutra</i>)
Bibliographic record
Abstract
In South Korea, the Eurasian otter (Lutra lutra (Linnaeus, 1758)), a semi-aquatic carnivore, is found mainly in lower order streams that tend to have a low abundance of preferred prey fish species. To investigate the relationship between resource use and availability, we used DNA barcoding to identify otter diet items in 24 otter spraints (faeces) from 16 sites along the Nakdong River basin from 4 to 6 June 2014. At these sites fish availability was assessed using scoop nets and casting nets. Fish formed the bulk of otter diet, which included also frogs, mammals, and reptiles. By DNA barcoding (success rate: 72.38%), we identified 79 prey items from 105 bone remains. The diet comprised mostly fish, but frogs, mammals, and reptiles were also identified. The fish fauna and otter diet composition differed significantly. Across the study sites, members of the Cyprinidae dominated in netted samples, but occurred less frequently in otter diet. Because most Cyprinidae are fast swimmers, otters also fed on benthic fishes and frogs, suggesting limited foraging flexibility in otters and specialization on more slowly moving prey.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".