Bibliographic record
Abstract
Just before they turn in to hibernate away the winter, thirteen-lined ground squirrels become little balls of fat. Piling on the calories to get them through the long fast, the rodents also accumulate a specialised form of fat, known as brown adipose tissue, which produces heat like a mini furnace to increase their body temperatures rapidly by 32°C when they dip in and out of their seasonal ‘slumber’. So, when Amanda MacCannell from the University of Western Ontario, Canada, and colleagues began monitoring how the mammals put on the pounds using MRI scans, they were surprised to notice a pad of fat around the optic nerve – just behind the eyeball – that tripled in size in the months leading up to hibernation. Realising that the pad resembled other regions of brown fat distributed around the ground squirrels’ bodies, the team began searching for evidence of the heat-generating protein (uncoupling protein 1), which is the hallmark of brown adipose tissue, to back up the hunch. However, after failing to find the protein, it was evident that the eye fat pad could not be a clandestine heater. Yet, the fatty deposit was somehow contributing to the rapid increase in the ground squirrels’ body temperature, as thermal images of the arousing animals showed the region near their eyes glowing hot almost an hour into the process.Wondering whether the pad could be a heat exchanger packed with blood vessels to retain heat that would otherwise be lost through the eyes, MacCannell scrutinised the pad under a microscope and discovered that it is threaded with the minute blood vessels that would be essential for preventing heat loss. ‘All of the evidence pointed towards a vascular rete [network]’, says MacCannell, who is keen to use CT scans to reveal whether the 3D arrangement of the blood vessels in the fatty pad could allow warmth that would otherwise seep from the body to be retained by blood heading to the brains of the reawakening rodents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".