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Record W2123210330 · doi:10.1242/jeb.014191

The scaling of safety factor in spider draglines

2008· article· en· W2123210330 on OpenAlexaff
Christine Ortlepp, John M. Gosline

Bibliographic record

VenueJournal of Experimental Biology · 2008
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpiderAraneusSILKBiologyZoologyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

This study documents the effect of body mass on the size and strength of draglines produced by the orb-weaving spider Araneus diadematus and the jumping spider Salticus scenicus. Silk samples obtained from individuals spanning the range from first-instar juveniles to gravid adults were tested to determine both the properties of the silk material and the strength and static safety factor of the draglines produced by each individual spider. Analysis of material properties indicates that the tensile strength and extensibility of the silks employed by each species are identical over the entire size range of the species. Analysis of the breaking forces for individual draglines, however, indicates that the draglines scale allometrically with the spider's body mass. For Araneus, breaking force (N) scales with body mass (kg) as Fmax=11.2M0.786, and the static safety factor (S(BW)=Fmax/Mg) scales as S(BW)=1.14M(-0.214). For Salticus, Fmax=0.363M0.66 and S(BW)=0.037M(-0.34). Thus, static safety factors decrease as these spiders grow, with values falling to 4-6 for adult Araneus and to 1-2 for adult Salticus. Analysis of these results suggests that the safety lines produced by these two species are not able to absorb the impact energy of most falls with a fixed length of pre-existing silk, except in the smallest of the Araneus spiders. It is therefore likely that both spiders must draw new silk from their spinnerets during falls to keep the dynamic loads on their safety-lines below failure levels.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.322
Teacher spread0.289 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations32
Published2008
Admission routes1
Has abstractyes

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