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Record W2149486978 · doi:10.1603/en11110

Influences on the Structure of Suburban Ant (Hymenoptera: Formicidae) Communities and the Abundance of Tapinoma sessile

2011· article· en· W2149486978 on OpenAlexaff
Tiffany Aurora Toennisson, Nathan J. Sanders, William E. Klingeman, Karen M. Vail

Bibliographic record

VenueEnvironmental Entomology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsHillsborough Hospital
Fundersnot available
KeywordsSpecies richnessAbundance (ecology)BiologyEcologyHabitatRelative species abundanceLitter

Abstract

fetched live from OpenAlex

Urbanization can alter the organization of ant communities and affect populations of urban pest ants. In this study, we sampled ant communities in urban and suburban yards to understand the habitat factors that shape these communities and influence the abundance of a common pest species, Tapinoma sessile (Say). We used pitfall traps to sample ant communities and a combination of pitfall traps and baiting to collect T. sessile at 24 sites in Knoxville, TN. In total, we collected 46 ant species. Ant species richness ranged from seven to 24 species per yard. Ant species richness tended to be lowest near houses, whereas T. sessile abundance was highest near houses. The best predictors of ant species richness in yards were canopy cover and presence of leaf litter: ant species richness peaked at mid-levels of canopy cover and was negatively correlated with the presence of leaf litter. Tapinoma sessile abundance increased with presence of logs, boards, or landscaping timbers and leaf litter in yards. Our results indicate that ant communities and the abundance of particular pest species in these urban and suburban landscapes are shaped by many of the same factors that structure ant communities in less anthropogenically disturbed 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.483

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 studies0.0000.001
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 head0.010
GPT teacher head0.206
Teacher spread0.195 · 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

Citations21
Published2011
Admission routes1
Has abstractyes

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