<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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".