Bibliographic record
Abstract
Imagine discovering an amphibious fish lurking inside a log. If you go down to the mangrove swamps of Central America during the dry season, you could find dozens of mangrove killifish (Kryptolebias marmoratus) packed together in the wet wood trying not to dry out. Normally, these fish are found in seasonal pools and flooded crab burrows but as their temporary homes dry out, they leap from the water in the hope of landing somewhere more moist. But what causes these normally solitary fish to huddle together with so many others in a log? It turns out that there are a few factors that can cause these amphibious fish to leave their homes – including the warmer temperatures resulting from climate change. Armed with this knowledge, Chloé Melanson and Simon Lamarre of the Université de Moncton, Canada, along with Suzanne Currie of Acadia University, Canada, wanted to know whether social interactions help these fish to tolerate the heat.The trio first raised individual fish in isolation before giving some fish a 24 h period of social interaction. To do this, Melanson and colleagues placed individual isolated fish in a container with mesh sides with rival killifish on the other side of the mesh. After their social interaction time was up, the team returned the fish to their isolated lives and slowly turned up the heat. As the water got hotter, the researchers noted the temperature when the fish's gills first emerged above the surface – a sign that the temperature was getting too hot for them – as well as the temperature when the fish could no longer stay upright in the water. The fish that had never had any social interaction started getting uncomfortably warm at lower temperatures than fish that had been introduced to others. However, both isolated and socialized killifish stopped being able to stay upright at similar temperatures. This suggests that there is something about being social that is making the fish stay longer in the warmer waters than they normally would. But what could cause this new tolerance to high temperatures?The researchers thought that having social interactions with other fish might be making the fish's heat sensors less sensitive. Interestingly, the protein that senses temperature changes in fish is the same one that detects spicy foods on human tongues. Given this knowledge, the team decided to test how spicy the water needed to be before the fish tried to flee the ‘hot’ water. They added capsaicin – the chemical responsible for the spiciness of chilli peppers – to the water and waited to see how much was needed before the fish started trying to escape. Astonishingly, the fish that had been socialized the day before could stand much higher levels of spice than the isolated fish, suggesting that their previous interactions with others has amplified their tolerance for heat.Melanson and the team are quick to point out that the increased ability of killifish to tolerate heat is not necessarily related to their heat sensors becoming less sensitive but the day of social interaction could have reduced the number of these heat-sensing proteins. Either way, it is important for scientists to understand how tropical fish react to heat if they are to predict how climate change will affect them in the future. In the case of the mangrove killifish, it might mean spending a lot more time being social.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".