A case study on collective intelligence based on energy flow
Bibliographic record
Abstract
In this paper, we propose a stochastic scheme for modeling a multi-species prey-predator artificial ecosystem in order to investigate the influence of energy flow on ecosystem lifetime and stability. Inhabitants of this environment are a few species of herbivore and carnivore birds. In this model, collective behavior emerges in terms of flocking, breeding, competing, resting, hunting, escaping, seeking, and foraging. Ecosystem is defined as a combination of prey and predator species with inter-competition among species within the same level of the food chain, and intra-competition among those belonging to different levels of the food chain. Some energy variables are also introduced as functions of behaviors to model the energy within the ecosystem. Experimental results of 11,000 simulations analyzed by Cox univariate analysis and hazard function suggest that only five corresponding energy variables out of eight aforementioned behaviors influence the ecosystem lifetime. Also, results of survival analysis show that among pairwise interactions between energy factors, only two interactions affect the system lifetime, including interaction between flocking and seeking energies, and interaction between flocking and hunting energies. These results match the observations of real life birds, which use flocking behavior for flexible movements, efficient foraging, social learning, and reducing predation risks.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".