The Realistic Modeling of Biological Systems: A Workshop Synopsis
Bibliographic record
Abstract
There is a growing awareness for the need to understand the basic design principles of living systems. In May of 2005, a diverse group of researchers from the fields of biomedicine, physics, mathematics, engineering, and computer science were brought together in Mizpe Hayamim, Israel to contemplate the current and future trends in computational modeling of biology. In the following work we provide an overview of the discussions that took place and describe some of the research projects that were presented. We also discuss how these seemingly disparate efforts may be integrated and directed at the development of meaningful computational models of biological systems. The wide range of techniques presented in Mizpe Hayamim served to demonstrate not only the breadth of scale found in biology but also the diversity in criteria for the development and application of numerical models in the field. One of the key issues remains the reconciliation of different model types and their effective use as a single composite representation. By attempting to formulate a unifying theme that transcends traditional boundaries between disciplines, it is hoped that this workshop provided a first rallying point that will promote a new level of interaction and synergy in the field.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".