Bibliographic record
Abstract
Locusts are used to sweltering temperatures, but sometimes their desert home is just too hot to handle. Corinne Rodgers and her colleagues at Queen's University, Ontario, are keen to know more about how locusts cope when the temperature rockets. They already knew that locusts handle extreme temperatures better when they've had a heat shock (a blast of higher temperatures) beforehand, and that daylength also influences how insects respond to heat. So, they wondered, how will locusts raised under two different daylengths respond to increased temperatures after a heat shock? To find out, the team first heat shocked locusts raised under 12 hours or 16 hours of daylight per day, then examined their ability to maintain a breathing rhythm as the temperature rose to a sizzling 45°C. 12 h locusts kept their cool: they maintained a stable breathing rhythm at higher temperatures than 16 h locusts, and when the rhythm broke down in extreme heat, 12 h locusts recovered quicker when the temperatures dropped again(p. 4690). Daylength,and heat shock, are important in helping locusts cope when the temperature soars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".