Data from: Human shields mediate sexual conflict in a top predator
Bibliographic record
Abstract
Selecting the right habitat in a risky landscape is crucial for an individual's survival and reproduction. In predator–prey systems, prey often can anticipate the habitat use of their main predator and may use protective associates (i.e. typically an apex predator) as shields against predation. Although never tested, such mechanisms should also evolve in systems in which sexual conflict affects offspring survival. Here, we assessed the relationship between offspring survival and habitat selection, as well as the use of protective associates, in a system in which sexually selected infanticide (SSI), rather than interspecific predation, affects offspring survival. We used the Scandinavian brown bear (Ursus arctos) population with SSI in a human-dominated landscape as our model system. Bears, especially adult males, generally avoid humans in our study system. We used resource selection functions to contrast habitat selection of GPS-collared mothers that were successful (i.e. surviving litters, n = 19) and unsuccessful (i.e. complete litter loss, n = 11) in keeping their young during the mating season (2005–2012). Habitat selection was indeed a predictor of litter survival. Successful mothers were more likely to use humans as protective associates, whereas unsuccessful mothers avoided humans. Our results suggest that principles of predator–prey and fear ecology theory (e.g. non-consumptive and cascading effects) can also be applied to the context of sexual conflict.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".