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Record W2167051951 · doi:10.1890/06-0995.1

MULTIPLE TECHNIQUES CONFIRM ELEVATIONAL DIFFERENCES IN INSECT SIZE THAT MAY INFLUENCE SPIDER SOCIALITY

2007· article· en· W2167051951 on OpenAlexaff
Jennifer Guevara, Leticia Avilés

Bibliographic record

VenueEcology · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpiderBiologyEcologyInsectPredationHabitatRainforestSociality

Abstract

fetched live from OpenAlex

Social and subsocial spiders of the genus Anelosimus exhibit an altitudinal pattern in their geographic distribution at tropical latitudes in the Americas. Social species, which capture prey cooperatively, occur primarily in the lowland rain forest and are absent from higher elevations, whereas subsocial species are common at higher elevations but absent from the lowland rain forest. Previous studies have suggested that differences in the size of potential insect prey along altitudinal gradients may explain this pattern as insects were found to be, on average, larger in lowland rain forests than at higher elevations. These studies, however, may have under-sampled the insect size composition of each habitat because only one sampling technique was used. Using a number of collection methods we sampled the insect size composition in the environments of social and subsocial spiders in this genus. We found that the average insect size in lowland rain forest habitats was indeed larger than at high-elevation cloud forests in eastern Ecuador. We also found that, even though the various techniques differed in the size of the insects they captured (visual searching and blacklighting yielding larger insects than beating, sweeping, or malaise trapping), they all caught, on average, larger insects in the lowlands. Overall, spider colonies in the lowlands caught larger prey than did spider colonies at higher elevations, paralleling differences in insect size distribution obtained by the various techniques in their respective 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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.925

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 head0.075
GPT teacher head0.257
Teacher spread0.182 · 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 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

Citations58
Published2007
Admission routes1
Has abstractyes

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