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Record W2012309675 · doi:10.1242/jeb.02544

NO TWO WAXES ARE THE SAME

2006· article· en· W2012309675 on OpenAlexaboutno aff
Kathryn Phillips

Bibliographic record

VenueJournal of Experimental Biology · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsNest (protein structural motif)WaxBiologyEcologyZoology

Abstract

fetched live from OpenAlex

Distinguishing friend from foe is as important for insects as it is for any other creature. You need to know when you're on someone else's territory and when an impostor is threatening. Many insects depend on their sense of smell to identify nest mates, and bees are no different; they recognise wax scents picked up by bees from their own nest, explains Robert Buchwald. However,wax's role in communication was probably a secondary evolutionary factor after its other main purpose: construction, where bees sculpt wax into nests and exquisite hexagonal combs to store honey and nurture larvae. Knowing that bees can distinguish wax scents carried by bees from other nests, Buchwald and Michael Breed wondered whether these subtle differences in composition also impacted on the material's structural properties. They decided to investigate waxes from several species to find out whether they were mechanically indistinguishable, or each had been honed to suit the structural needs of each species' nests (p. 3984). The pair teamed up with mechanical engineer Alan Greenberg to measure several waxes' mechanical properties, but first they needed nests to test.Buchwald explains that getting hold of Apis mellifera nests was straightforward; he simply visited the apiary at the University of Colorado at Boulder. However, tracking down the more exotic Apis species was much trickier. Fortunately, the team established a strong collaboration with Canadian scientist Gard Otis, who supplied them with nests during his field work in Asia, despite running the constant gauntlet of bee stings.Melting down the nests, Buchwald and Greenberg cast each species' wax into a cylinder shape to remove the nests' architectural differences, before compressing the wax to test its structural properties. But working with the soft wax samples was very different from the construction materials that Greenberg usually studied; the team had to find the most sensitive stress detector for the compression system that they used to calculate each waxe's mechanical strength and stiffness.Analysing the results, the team realised that Apis dorsata's wax was by far the strongest and stiffest, while Apis andreniformis's was the weakest and softest. Each species' wax was mechanically unique and unlike the other three's.Buchwald suspects that the bees' nesting habits could account for the mechanical differences. He explains that Apis dorsata's colossal nests not only have to support the greatest weight, but also must withstand knocks and high winds in their exposed locations, suspended from tree branches high above the forest canopy. Meanwhile, Apis cerana and Apis mellifera build their nest combs in protected cavities, such as dead trees, which seems to have resulted in the insects evolving intermediate-strength waxes. However, Buchwald was most surprised by Apis andreniformis's wax. He explains that, like Apis dorsata,andreniformis hangs its nests from tree branches. But andreniformis nests are much smaller than hefty dorsata's and are located in the relative protection of the forest's lower reaches,hanging from springy branches that protect the nests from mechanical shocks. Buchwald suspects that these differences in lifestyle have allowed andreniformis to evolve softer wax than dorsata's robust blend.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.012
Scholarly communication0.0090.015
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0330.018

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 head0.010
GPT teacher head0.287
Teacher spread0.277 · 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
Published2006
Admission routes1
Has abstractyes

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