The Whole is Greater Than the Sum of Its Parts: Large-scale Phenomena Arising from Small-scale Biophysical Processes
Bibliographic record
Abstract
The symposium "Large-scale biological phenomena arising from small-scale biophysical processes" at the SICB 2023 Annual General Meeting focused on the cross-disciplinary exploration of emergent phenomena in biology. Interactions between cells or organisms at small scales within a system can govern patterns occurring at larger scales in space, time, or biological complexity. This theme recurs in many sub-disciplines of biology, including cell and developmental biology, evolution, and ecology. This symposium, and the associated special issue introduced here, showcases a wide range of cross-disciplinary collaborations among biologists, physicists, and engineers. Technological advancements in microscopy and microfluidics, as well as complementary advances in mathematical modelling and associated theory demonstrate the timeliness of this issue. This introduction seeks to provide useful background information to place the studies within this issue in a broader biophysical context and highlight similarities in ideas and approaches across systems and sub-disciplines. We hope to demonstrate that cross-disciplinary research linking small-scale biophysics to larger-scale emergent phenomena can help us understand problems ranging from single-cell behaviors to tissue formation and function, evolution of form, and the dynamics of communities.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".