<scp>Nemo‐age</scp>: Spatially explicit simulations of eco‐evolutionary dynamics in stage‐structured populations under changing environments
Bibliographic record
Abstract
Abstract Anticipating and preparing for the effect of environmental changes on biodiversity requires to understand and predict both the ecological and evolutionary responses of populations. Tools and methods to efficiently integrate these complex processes are lacking. We present the genetically and spatially explicit individual‐based simulation software Nemo‐age combining ecological and evolutionary processes. Nemo‐age has a strong emphasis on modelling complex life histories. We here provide a methodology to predict changes in species distribution for given climate projections using Nemo‐age . Modelling complex life histories, spatial distribution and evolutionary processes unravel possible eco‐evolutionary mechanisms that have been previously overlooked when populations endure rapid environmental changes. The interface of Nemo‐age is designed to integrate species' data from different fields, from demography to genetic architecture and spatial distributions, thus representing a versatile tool to model a variety of applied and theoretical scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".