<em>C. elegans</em> Tracking and Behavioral Measurement
Bibliographic record
Abstract
We have developed instrumentation, image processing, and data analysis techniques to quantify the locomotory behavior of C. elegans as it crawls on the surface of an agar plate. For the study of the genetic, biochemical, and neuronal basis of behavior, C. elegans is an ideal organism because it is genetically tractable, amenable to microscopy, and shows a number of complex behaviors, including taxis, learning, and social interaction. Behavioral analysis based on tracking the movements of worms as they crawl on agar plates have been particularly useful in the study of sensory behavior, locomotion, and general mutational phenotyping. Our system works by moving the camera and illumination system as the worms crawls on a stationary agar plate, which ensures no mechanical stimulus is transmitted to the worm. Our tracking system is easy to use and includes a semi-automatic calibration feature. A challenge of all video tracking systems is that it generates an enormous amount of data that is intrinsically high dimensional. Our image processing and data analysis programs deal with this challenge by reducing the worms shape into a set of independent components, which comprehensively reconstruct the worms behavior as a function of only 3-4 dimensions. As an example of the process we show that the worm enters and exits its reversal state in a phase specific manner.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".