A <em>Caenorhabditis elegans</em> Nutritional-status Based Copper Aversion Assay
Bibliographic record
Abstract
To ensure survival, organisms must be capable of avoiding unfavorable habitats while ensuring a consistent food source. Caenorhabditis elegans alter their locomotory patterns upon detection of diverse environmental stimuli and can modulate their suite of behavioral responses in response to starvation conditions. Nematodes typically exhibit a decreased aversive response when removed from a food source for over 30 min. Observation of behavioral changes in response to a changing nutritional status can provide insight into the mechanisms that regulate the transition from a well-fed to starved state. We have developed an assay that measures a nematode's ability to cross an aversive barrier (i.e. copper) then reach a food source over a prolonged period of time. This protocol builds upon previous work by integrating multiple variables in a manner that allows for continued data collection as the organisms shift towards an increasingly starved condition. Moreover, this assay permits an increased sample size so that larger populations of nematodes can be simultaneously evaluated. Organisms defective for the ability to detect or respond to copper immediately cross the chemical barrier, while wild type nematodes are initially repelled. As wild type worms are increasingly starved, they begin to cross the barrier and reach the food source. We designed this assay to evaluate a mutant that is incapable of responding to diverse environmental cues, including food sensation or detection of aversive chemicals. When evaluated via this protocol, the defective organisms immediately crossed the barrier, but were also incapable of detecting a food source. Hence, these mutants repeatedly cross the chemical barrier despite temporarily reaching a food source. This assay can straightforwardly test populations of worms to evaluate potential pathway defects related to aversion and starvation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".