Bibliographic record
Abstract
Caterpillars come in all shapes, sizes and colours. Some are furry, others have stripes, some even have eye-shaped spots, and it turns out that a large number also have great hearing. ‘Over 30 different species of caterpillars have been noted to respond to a variety of sounds including the human voice, clapping, tuning forks, doors slamming, highway noise and jet aircraft’, says Jayne Yack from Carleton University, Canada. Yet, despite all their thrashing, rearing and freezing on the spot in response to sounds, it wasn't clear what caterpillars are listening out for. ‘While we are aware that many caterpillars respond to sounds, we know little about how or why they hear’, says Yack. As the caterpillars of monarch butterflies (Danaus plexippus) freeze, shorten their bodies and flick their front ends in response to a tuning fork pitched at 250 Hz, Yack and Chantel Taylor decided to find out which frequencies the yellow, white and black caterpillars tune into and how they hear.Playing short tones ranging in pitch from 50 to 1200 Hz to the caterpillars, Taylor assessed which frequencies set the insects thrashing defensively. Plotting the caterpillars’ reactions on a graph, it was clear that the insects were most sensitive to tones between 100 and 200 Hz, although their hearing ranged from 50 Hz up to 900 Hz. Then, Taylor began searching the insects' bodies for evidence of their ‘ears’. Knowing that Dwight Minnich had suggested in the 1930s that hairs on the insect's body might pick up sounds, Taylor focused on three structures: seven pairs of vibration-sensitive hairs (filiform trichoid sensillae) distributed along the caterpillar's sides, the prothoracic shields on its back and fleshy structures, known as tubercles, at two locations on its flanks. Surgically removing each structure and then testing the caterpillars’ hearing, Taylor quickly ruled out the involvement of the tubercules and shield structures, before eventually narrowing in on two sensilla on the thorax portion (near the head) of the caterpillar's body. And when she visualised the structures with an electron microscope, she could see a 0.5 mm long hair, which would be ideal for detecting sound waves carried by vibrating air molecules.But why have monarch butterflies evolved hearing when they aren't exactly known for their conversation? ‘We propose that hearing in monarch larvae functions to protect against aerial insect parasitoids and predators’, says Yack, who explains that the hefty larvae are prone to being consumed from the inside out by the larvae of parasitic insects that lay their eggs on the hapless caterpillars. Yack suggests that the caterpillars may freeze when they hear pitches that sound like an approaching insect to avoid detection, but resort to flailing around when they are at risk of an impending attack. However, Taylor also discovered that the caterpillars can be lulled into a false sense of security, losing interest in lashing out after hearing the same buzz five times, which could place the insects at more risk from predators in noisy environments, such as railways and airports. But Yack adds that forcing pest caterpillars with hearing to drop their defences by repeatedly buzzing them could form the basis of novel green forms of pest control by leaving them vulnerable to predators that might otherwise be vanquished by a quick flick of the head.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".