Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».