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Record W6892147234 · doi:10.5061/dryad.486155q

Data from: The shortfall of sociality: group-living affects hunting performance of individual social spiders

2018· dataset· en· W6892147234 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2018
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSocialityForagingPredationSocial groupGroup livingCooperative breedingVariation (astronomy)

Abstract

fetched live from OpenAlex

Ineffective hunters in cooperative foraging groups may be shielded from natural selection by their more effective group mates, whereas those living solitarily would starve and thus be removed from the population. The problem may be exacerbated in large groups where it may be easier for individuals to withhold participation. Group foragers may thus be ineffective individual hunters or exhibit greater inter-individual variation in hunting abilities, in particular when living in large groups. We test these hypotheses in spider species of the genus Anelosimus that differ in their level of sociality and, among social species, in colonies of different sizes. We found that individuals from the more social species, and those from larger groups, reacted more slowly to prey than those from solitary species or small groups. Individuals from these categories also had greater inter-individual variation in reaction times. Individuals from large social groups also had lower prey capture success than those from small ones. These differences may have been driven by the size of the group from which the social individuals were taken, as those from small colonies behaved similarly to individuals of the two less social species. This finding suggests that hunting ability may develop as a phenotypically plastic trait.

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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.056
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0560.042

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 head0.062
GPT teacher head0.274
Teacher spread0.212 · 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
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

Citations0
Published2018
Admission routes1
Has abstractyes

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