In the great pond snail (<i>Lymnaea stagnalis</i>), two stressors that individually enhance memory in combination block memory formation
Bibliographic record
Abstract
Stress plays an important role in memory formation in the great pond snail (Lymnaea stagnalis (Linnaeus, 1758)). Individual stressors have been shown to enhance or to perturb long-term memory (LTM) formation. However, when snails perceive a combination of two stressors, it is unclear the outcome with regards to LTM formation. Here we first show that when L. stagnalis are exposed individually to either a predator stressor (crayfish effluent (CE), which is a kairomone) or a thermal stressor (30 °C), LTM formation is enhanced. In their natural environment, L. stagnalis may experience temperatures approaching 30 °C and they may encounter crayfish at the same time. How such a combination of stressors alters adaptive behaviour is unknown. Here we show that when these two stressors are combined, LTM formation is blocked. Since boiling CE inactivates the kairomone, we used previously boiled CE that we combined with the thermal stressor and found that LTM formation is again enhanced. These data show that (i) it cannot accurately be predicted how a combination of stressors when combined interact to alter LTM formation and (ii) there is a difference between hot CE and room temperature CE.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".