Ecological Effects of Cyclically Fluctuating Resources
Bibliographic record
Abstract
In nature, the resources available to organisms in an ecosystem fluctuate regularly due to abiotic factors like weather patterns and seasonality, and are also impacted by humans. The effects of resource fluctuation or stability have been studied in real ecosystems at small physical and temporal scale because of physical and temporal constraints on researchers. In order to study these phenomena at much larger scales, we employ EcoSim, an individual-based predator-prey ecosystem model designed for investigating ecological and evolutionary phenomena at very large scales. In this paper, we modified EcoSim such that resources available to prey fluctuate cyclically, so that we can observe the behavioral, population-wide, and evolutionary impacts of cyclically fluctuating resources. The runs have computed 2000 time-steps, and our goal is to have 20000 time-steps in order to observe evolutionary consequences. We can already observe several effects of resource fluctuations. We observe oscillations in reproductive success and failures, energy spent per time-step per individual, population sizes, and compactness, that are all synchronized with the fluctuations in resources.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".