Bibliographic record
Abstract
In biology, the importance of environmental feedback to the process of embryogenesis is well understood. In this paper we explore the introduction of a local fitness to an artificial developmental system, providing an artificial analogue to the natural phenomenon. First, we define a highly simplified model of vasculogenesis, an environment-based toy problem in which we can evaluate our strategies. Since the use of a global fitness function for local feedback is likely too computationally expensive, we introduce the notion of a neighbourhood-based ldquolocal fitnessrdquo function. This local fitness serves as an environmental-feedback guide for the developmental system. The result is a developmental analogue of guided hill-climbing, one which significantly improves the performance of an artificial embryogeny in the evolution of a simplified vascular system. We further evaluate our model in a collection of randomly generated two-dimensional geometries, and show that inclusion of local fitness helps allay some of the problem difficulty in irregular environments. In the process, we also introduce a novel and systematic means of generating bounded, connected two-dimensional geometries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".