Molecular motors: how to make models that can be used to convey the concept of molecular ratchets and thermal capture
Bibliographic record
Abstract
A wide variety of cellular processes use molecular motors, including processive motors that move along some form of track (e.g., myosin with actin, kinesin or dynein with tubulin) and polymerases that move along a template (e.g., DNA and RNA polymerases, ribosomes). In trying to understand how these molecular motors actually move, many apply their understanding of how man-made motors work: the latter use some form of energy to exert a force or torque on its load. However, quite a different mechanism has been proposed to possibly account for the movement of molecular motors. Rather than hydrolyzing ATP to push or pull their load, they might use their own thermal vibrational energy as well as that of their load and their environment to move the load, capturing those movements that occur along a desired vector or axis and resisting others; ATP hydrolysis is required to make backward movements impossible. This intriguing thermal capture or Brownian ratchet model is relatively more difficult to convey to students. In this report, we describe several teaching aids that are very easily constructed using widely available household materials to convey the concept of a molecular ratchet.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".