Bibliographic record
Abstract
As any real estate agent will tell you, there are many important factors to consider when choosing a home. Is the neighbourhood safe? Are there plenty of options for food nearby? Will it be easy to start a family? Many oceanic animals start out life as pelagic larvae, travelling vast distances on currents before they find a suitable location on which to settle and metamorphose into their more sedentary adult form. For these larvae, selecting a home seems an overwhelming task. How can such small animals, at the mercy of tides and currents, select the correct habitat? How can they even discriminate among different options?Jenni Stanley, Craig Radford and Andrew Jeffs from the University of Auckland's Leigh Marine Laboratory, New Zealand, set out to investigate how larval crabs are able to select an appropriate site for metamorphosis. The researchers hypothesized that as oceanic larvae don't have particularly good vision, and chemical cues drift with currents, larval crabs might be using sound cues to locate the ideal settlement site. Sound is particularly promising as a signal of habitat quality because it carries clearly underwater and provides a consistent directional cue that larvae can home in on.To investigate this possibility, the researchers captured the larvae of two species of temperate crab off the coast of New Zealand (Hemigrapsus sexdentatus and Cyclograpsus lavauxi), and three species of tropical crab off the coast of Australia (Cyno andreossyi, Schizophrys aspera and Grapsus tenuicrustatus). Returning to the lab, the trio housed the larvae in individual vials, each with a roughened floor on which the larvae could metamorphose. Then, they isolated the vials in soundproof waterbaths and exposed the larvae to sounds recorded in the larvae's natural environment: continuous sound from a high quality reef habitat, sound recorded from a moderate broken reef or mixed beach habitat, the sound of a poor quality open sand habitat, or silence. Finally, the researchers moored some of the vials containing larval residents close to the field locations where they had recorded the sounds, then regularly checked the laboratory and field vials to see how the larvae fared. The question was, would the crab larvae take advantage of a prime real estate opportunity and metamorphose more quickly when moored near a desirable location than when moored near a poor quality habitat? And would exposure to the sound alone cause this effect, or would they require the full spectrum of environmental cues to trigger rapid metamorphosis?After a week, the team realized that the larval crabs must have good instincts for real estate, because all five species discriminated well among the habitat types. When exposed to the full spectrum of environmental cues in the wild, and when listening to the sound recordings in the laboratory, the larvae metamorphosed more quickly when they heard the high quality site than when they heard a lesser quality habitat or silence. This is probably a highly conserved response across crabs, as all five species, from three different families and two different climate zones, had the same response to sound cues.When the scientists analysed the sound recordings, they found that the presence or absence of sounds generated by noisy marine animals, such as sea urchins and snapping shrimp, caused the main auditory differences between the habitat types. It is an intriguing possibility that the larval crabs are listening for potential neighbours as they drift past a given habitat. Rather than relying on real estate agents, larval crabs appear to choose their homes based on word-of-mouth recommendations from prospective neighbours.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.166 | 0.075 |
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".