Testing measures of animal social association by computer simulation
Bibliographic record
Abstract
Abstract Techniques used to measure patterns of affiliation among social animals have rarely been tested for accuracy. One reason for this lack of validation is that it is often impossible to compare sample data to the true distribution of social assortment of a group of animals. Here we test some methods of assessing social assortment by using a computer simulation of organisms whose assortment patterns were under our control. We created male and female organisms that moved in a direction that was based on a social bias parameter. As the weight of this parameter increased, organisms were more likely to move in the direction of others of their sex. We then created virtual observers to sample assortment of the organisms under different social bias conditions. Observers used three different techniques of measuring assortment. These were (1) group membership: noting all organisms that were associated in the same 'group', (2) nearest neighbour: noting the nearest organism to a randomly selected individual and (3) neighbourhood: noting all organisms near a selected individual. Neighbourhood was taken either by all-occurrence sampling or by focal sampling the associations of randomly selected individuals. Some techniques emerged as more sensitive than others under different conditions and biases were revealed in some measures. For example, the group membership method was biased toward finding significant assortment differences between the sexes when no difference actually existed. Nearest neighbour was insensitive to finding a difference in assortment between sexes when one existed. Focal sampling was less sensitive to finding effects than all-occurrence sampling. The computer simulation revealed properties of each technique that would have been impossible to detect in the field.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".