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Record W6967157708 · doi:10.5061/dryad.n6d4cp2

Data from: Better safe than sorry: spider societies mitigate risk by prioritizing caution

2019· dataset· en· W6967157708 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2019
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPopulationSpiderPredationPredatorImmigrationSocial group

Abstract

fetched live from OpenAlex

Group members often vary in the information that they have about their environment. In this study, we evaluated the relative contribution of information held by the population majority vs. new immigrants to groups in determining group function. To do so we created experimental groups of the social spider Stegodyphus dumicola that were either iteratively exposed to a dangerous predator, the ant Anoplopepis custodiens, or kept in safety. We then seeded these groups (i.e., the population majority) with an “immigrant” individual that either had or did not have prior experience with the predator and was either shy or bold. Bold group members are argued to be particularly influential for group function in S. dumicola. We then evaluated colonies’ response towards predators over multiple trials to determine the effect of the immigrant’s and the majority’s prior experience with the predator and the immigrant’s boldness. We found that groups adopt a “better safe than sorry” strategy, where groups avoided predators when either the group or the immigrant had been previously exposed to risk, regardless of immigrant boldness. These findings suggest that past experience with predators, even if only experienced by a single individual in the group, can alter how groups respond to risk in a potentially advantageous manner.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.221
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.003
Research integrity0.0000.001
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 head0.039
GPT teacher head0.256
Teacher spread0.217 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2019
Admission routes1
Has abstractyes

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