Ants build transportation networks that optimize cost and efficiency at the expense of robustness
Bibliographic record
Abstract
Like modern human societies, many biological systems are dependent on transportation networks for the efficient distribution of resources and information. Network builders face the daunting challenge of optimizing conflicting network criteria such as robustness, efficiency, and cost, which cannot be optimized simultaneously. Here, we use graph and network theory to examine the trail networks of the polydomous meat ant Iridomyrmex purpureus . Meat ants build and maintain physical trails that connect their multiple nests to each other and to food resources. The resulting transportation network is used to distribute workers, brood, and food resources. We found that meat ants built low-cost trail networks that were relatively efficient. However, networks were less robust than comparable simulated networks, suggesting that meat ants prioritize cost and efficiency over robustness. Populous nests had higher connectivity than did less populous nests, implying they play a key role in resource distribution throughout the network. We propose that meat ant networks are an ideal model system for the development of network optimization heuristics.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".