High Density or Urban Sprawl: What Works Best in Biology?
Bibliographic record
Abstract
With new approaches in imaging-from new tools or reagents to processing algorithms-come unique opportunities and challenges to our understanding of biological processes, structures, and dynamics. Although innovations in super-resolution imaging are affording novel perspectives into how molecules structurally associate and localize in response to, or in order to initiate, specific signaling events in the cell, questions arise as to how to interpret these observations in the context of biological function. Just as each neighborhood in a city has its own unique vibe, culture, and indeed density, recent work has shown that membrane receptor behavior and action is governed by their localization and association state. There is tremendous potential in developing strategies for tracking how the populations of these molecular neighborhoods change dynamically.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".