Bibliographic record
Abstract
We all know an animal is the product of its genes and the influence of the environment. Humidity, food availability and parental care are just examples of factors that can have a strong impact on an animal's characteristics. This is certainly true in the case of reptiles, where incubation temperature can affect several of their physical and physiological characteristics. It is well known, for example, that sex, size and running speed can all be influenced by temperature in various lizard species. But what about other, less-obvious characteristics? It is harder to measure how incubation temperature affects different aspects of behaviour and intelligence. None the less, these are interesting questions and Joshua Amiel and Richard Shine from the University of Sidney, Australia, decided they were worth pursuing.They wanted to know whether incubation temperature affected young lizards' ability to learn. Moreover, they wanted to know whether an increased ability to learn would be relevant during a life-threatening situation. To do this, Amiel and Shine collected gravid three-lined skinks, which are lizards native to Australian forests. They took them back to their lab in Sidney and incubated their eggs at cold (16±7.5°C) and hot (22±7.5°C) regimes. When the babies hatched, it was time for their IQ tests inside their own homes so that they would not have to deal with the additional stress of learning in a strange environment. Each enclosure had two small hiding retreats but the entrance to one of them was blocked with a piece of Plexiglas. For each test a baby lizard was placed right in the middle between the two retreats under a little plastic cover. As soon as the experimenter lifted the cover, he tickled the lizard's tail with a paintbrush, scaring the poor lizard, which ran for a hiding place. The scientists then counted the number of times that the hatchling chose the wrong hide and the time it took to finally find the available retreat. They repeated these trials for 4 days, four times a day with each lizard and then compared the reptiles' success rates through time. Were they learning?All the babies were capable of learning and made fewer mistakes the more tests they performed, but as time went by, the hatchlings that had been incubated in the hot environment learned faster and made fewer mistakes than the cold-incubated ones. This was independent of sex, size or running speed.The fact that warm-incubated lizards turned out to be smarter than cold-incubated lizards gives a glimpse of what might happen during global warming. Amiel and Shine point out that this increased ability to learn will increase the lizard's capacity to respond in the face of environmental changes and therefore increase their chances of survival. The study does not dismiss the possibility that cold-incubated lizards may ‘catch up’ with their warm-incubated counterparts as they develop, or even compensate for their slower wits with other abilities such as locomotor speed. However, this does not negate the fact that lizards from hot eggs have the edge over their cool-egg friends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.005 | 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; both teacher heads agree on what is shown here.
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".