How to reduce fear in a snail: Take an aspirin, call me in the morning
Bibliographic record
Abstract
Aspirin (Acetylsalicylic acid, ASA), one of the widely used non-steroid anti-inflammatory drugs can easily end up in sewage effluents and thus it becomes necessary to investigate the effects of aspirin on behaviour of aquatic organisms. Previous studies in mammals have shown ASA to alter fear and anxiety-like behaviours. In the great pond snail Lymnaea stagnalis, ASA has been shown to block a 'sickness state' induced by lipopolysaccharide injection which upregulates immune and stress-related genes thus altering behavioural responses. In Lymnaea, eliciting physiological stress may enhance memory formation or block its retrieval depending on the stimulus type and intensity. Here we examine whether ASA will alter two forms of associative-learning memory in crayfish predator-experienced Lymnaea when ASA exposure accompanies predator-cue-induced stress during the learning procedure. The two trainings procedures are: 1) operant conditioning of aerial respiration; and 2) a higher form of learning, called configural learning, which here is dependent on evoking a fear response. We show here that ASA alone does not alter homeostatic aerial respiration, feeding behaviour or long-term memory (LTM) formation of operantly conditioned aerial respiration. However, ASA blocked the enhancement of LTM formation normally elicited by training snails in predator cue. ASA also blocked configural learning, which makes use of the fear response elicited by the predator cue. Thus, ASA alters how Lymnaea responds cognitively to predator detection.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".