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Record W2319224199 · doi:10.1242/jeb.064360

THE NEIGHBOURHOOD SOUNDS NICE

2012· article· en· W2319224199 on OpenAlexaff
Constance M. O’Connor

Bibliographic record

VenueJournal of Experimental Biology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPelagic zoneLarvaEcologyHabitatNeighbourhood (mathematics)FisheryBiologyGeography

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.166
Threshold uncertainty score0.554

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0070.003
Scholarly communication0.0070.006
Open science0.0010.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.1660.075

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.

Opus teacher head0.014
GPT teacher head0.277
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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