Bibliographic record
Abstract
Kleptoparasitism is an important means by which many animals obtain limited resources. The success of kleptoparasitism may be influenced by a number of factors, including competitive differences among individuals and the spatial distribution of prey and hosts. I used ideal free distribution (IFD) theory to predict the spatial distribution of kleptoparasites and their hosts between two patches differing in quality and to predict how the use of kleptoparasitism was influenced by the relative searching and fighting abilities of classes of competitors. Unlike previous IFD models incorporating kleptoparasitism, I allowed competitors to choose between attempting kleptoparasitism or searching for undefended prey. When the rates of resource inputs into the patches were high, the model predicted little use of kleptoparasitism. If competitive types were equally able to displace others from resources, then those individuals that were poorer at searching for food were more likely to kleptoparasitize. If competitive types differed in their abilities to displace others, kleptoparasites were exclusively those individuals that were best able to do so. Regardless of their competitive type, a higher proportion of individuals in the high-quality patch were kleptoparasitic, while the total density of competitors in the high-quality patch was lower than that expected based on the ratio of resource inputs. These predictions differ from previous IFD models of kleptoparasitism, suggesting that the mechanisms involved in searching for and obtaining resources can influence the spatial distribution of animals.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".