The influence of resource seasonality on the breeding patterns of the Eurasian otter (<i>Lutra lutra</i>) in Mediterranean habitats
Bibliographic record
Abstract
Litters of small Eurasian otter (Lutra lutra) cubs ranged from one to four, with those of one and two accounting for 95%. Significant variations were found between locations and according to the main diet (average ranging between 1.1 and 2.4 cubs/female). We found a seasonal pattern in otter breeding in some areas, being different in each. In the Prepyrenees, most births took place between March and June (85%). In Mediterranean rivers of the Ebro basin, most births occurred between December and February (57%). In both, the small cubs were found outside the dens just 23 months after the time of birth. Fish and crayfish exhibited a seasonal fluctuation, with a maximum density of biomass between the end of spring and the end of summer and minimum densities in winter. Water was always flowing in the Pyrenees and Prepyrenees rivers; however, in Mediterranean rivers, important periods of drought were observed, concentrated especially in summer and some winters. Timing of birds corresponded to variation in abundance of food (energy needs) and water resources in space and time. The presence of adequate prey species for the cubs (Ebro's barbel (Barbus graellsii and Barbus haasi) and American crayfish (Procambarus clarkii) in our study area) plays an important role. Interannual variations in food can affect the otter's reproductive cycle and breeding success.
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 | 0.001 |
| 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 | 0.000 | 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".