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Record W2613724610 · doi:10.5061/dryad.m78b6

Data from: Rain, predators, and spider sociality: a manipulative experiment

2016· article· en· W2613724610 on OpenAlexaff
Hoffman Catherine, Leticia Avilés

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSocialitySpiderPredationEcologyGeographyBiology

Abstract

fetched live from OpenAlex

Group-living organisms offer a unique perspective on how environmental gradients influence geographic distributions, as not only the properties of individuals, but also those of their groups interact with the environment to determine a species range. In turn, the ranges of group-living organisms should provide insights on the conditions that favor group versus solitary living. Here we show that rain intensity and predation by ants, factors postulated to exclude subsocial Anelosimus spiders from the lowland tropical rainforest, are greater in this habitat than at higher elevations. We further show that experimentally excluding these factors increases the survival of subsocial Anelosimus colonies when transplanted to the lowland rainforest, but not at their native higher elevation range. While providing a rare experimental test of the simultaneous importance of abiotic and biotic gradients on species range limits, these results provide direct evidence that adverse environmental factors may prevent solitary living and require group living in certain environments.

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)0.005
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: Dataset · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.049
GPT teacher head0.303
Teacher spread0.254 · 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
Published2016
Admission routes1
Has abstractyes

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